Basic Information

Affiliated programs (koza)

• Graduate School of Pharmaceutical Sciences, 医薬創成情報科学専攻 医薬創成情報科学講座, 教授

Concurrent Affiliation

• Center for the Promotion of Interdisciplinary Education and Research (C-PiER), 統合複雑系科学国際研究ユニット

Academic Degree

• Master of Engineering (University of Tokyo)
• PhD in Information Sciences (University of Tokyo)

Research History

• From Apr. 2005, To Present

  Kyoto University, Bioinformatics Center, Institute for Chemical Research, Professor
• From Apr. 2002, To Mar. 2005

  Kyoto University, SGI Donation Laboratory, Institute for Chemical Research, Visiting Associate Professor
• From Apr. 1991, To Mar. 2002

  NEC Corporation, Research Staff Member

ID,URL

• ORCID ID

  0000-0002-6607-5617

• J-Global ID

  201401016443401433

• External link

  https://www.bic.kyoto-u.ac.jp/pathway/mami/index.html

Website(s) (URL(s))

• http://www.bic.kyoto-u.ac.jp/pathway/mami

researchmap URL

• https://researchmap.jp/7000008516

Research

Research Topics, Overview of the research

• Research Topics

  Machine learning research for accelerating / advancing science and engineering, particularly life sciences

• Overview of the research

  My machine learning research has two aspects, being featured by "data": 1) graphs and networks: a typical and emerging graph in our present society is social networks in our life, and also a variety of graphs in modern sciences, for instance, gene regulatory networks, medical life pathways and chemical (or molecular) structures. I have developed machine learning methods to obtain knowledge or information from this type of graphs. 2) data integration: in general, any entity can be found in not only one but multiple data sources. For example, you are connected by social networks, and your purchase record in an e-commerce site would be kept in the e-commerce company. This is also the case with your online surfing record, such as your seen movies at youtube. Then your demographic data, such as age, sex, the region in which you were born and currently you are living, can be saved in many places on-line. So we can use your data from many sources. The question is how we can integrate this type of different and diverse data to build a good machine learner to predict your unforeseen future. To answer this question, I have done research on data-integrative machine learning.

Research Interests

• data-driven science
• bioinformatics
• machine learning

Research Areas

• Informatics, Intelligent informatics

Papers

• Sc2Mol: a scaffold-based two-step molecule generator with variational autoencoder and transformer

  Zhirui Liao; Lei Xie; Hiroshi Mamitsuka; Shanfeng Zhu

  Bioinformatics, 28 Dec. 2022, Peer-reviewed

  
• Central-Smoothing Hypergraph Neural Networks for Predicting Drug–Drug Interactions

  Duc Anh Nguyen; Canh Hao Nguyen; Hiroshi Mamitsuka

  IEEE Transactions on Neural Networks and Learning Systems, 2023

  
• DeepMHCII: A novel binding core-Aware deep interaction model for accurate MHC-II peptide binding affinity prediction

  Ronghui You; Wei Qu; Hiroshi Mamitsuka; Shanfeng Zhu

  Bioinformatics, 01 Jul. 2022, Peer-reviewed

  
• SPARSE: a sparse hypergraph neural network for learning multiple types of latent combinations to accurately predict drug–drug interactions

  Duc Anh Nguyen; Canh Hao Nguyen; Peter Petschner; Hiroshi Mamitsuka

  Bioinformatics, 24 Jun. 2022, Peer-reviewed, Last author

  
• HPODNets: deep graph convolutional networks for predicting human protein-phenotype associations

  Lizhi Liu; Hiroshi Mamitsuka; Shanfeng Zhu

  Bioinformatics, 01 Feb. 2022, Peer-reviewed

  
• Drug3D-DTI: Improved Drug-target Interaction Prediction by Incorporating Spatial Information of Small Molecules

  Zhirui Liao; Xiaodi Huang; Hiroshi Mamitsuka; Shanfeng Zhu

  2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 09 Dec. 2021, Peer-reviewed

  
• Crop loss identification at field parcel scale using satellite remote sensing and machine learning

  Santosh Hiremath; Samantha Wittke; Taru Palosuo; Jere Kaivosoja; Fulu Tao; Maximilian Proll; Eetu Puttonen; Pirjo Peltonen-Sainio; Pekka Marttinen; Hiroshi Mamitsuka

  PLoS ONE, Dec. 2021, Peer-reviewed, Last author

  
• Machine learning approaches for drug combination therapies

  Betül Güvenç Paltun; Samuel Kaski; Hiroshi Mamitsuka

  Briefings in Bioinformatics, 06 Aug. 2021, Peer-reviewed, Last author

  
• DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction

  Ronghui You; Shuwei Yao; Hiroshi Mamitsuka; Shanfeng Zhu

  Bioinformatics, 04 Aug. 2021, Peer-reviewed

  
• Learning subtree pattern importance for Weisfeiler-Lehman based graph kernels

  Dai Hai Nguyen; Canh Hao Nguyen; Hiroshi Mamitsuka

  Machine Learning, 13 Jun. 2021, Peer-reviewed

  
• BERTMeSH: deep contextual representation learning for large-scale high-performance MeSH indexing with full text

  Ronghui You; Yuxuan Liu; Hiroshi Mamitsuka; Shanfeng Zhu

  Bioinformatics, 05 May 2021, Peer-reviewed

  
• HPOFiller: identifying missing protein–phenotype associations by graph convolutional network

  Lizhi Liu; Hiroshi Mamitsuka; Shanfeng Zhu

  Bioinformatics, 06 Apr. 2021, Peer-reviewed

  
• Reshaped tensor nuclear norms for higher order tensor completion

  Kishan Wimalawarne; Hiroshi Mamitsuka

  Machine Learning, Mar. 2021, Peer-reviewed

  
• XGSEA: CROSS-species gene set enrichment analysis via domain adaptation

  Menglan Cai; Canh Hao Nguyen; Hiroshi Mamitsuka; Limin Li

  Briefings in Bioinformatics, 30 Jan. 2021, Peer-reviewed

  
• Improving drug response prediction by integrating multiple data sources: matrix factorization, kernel and network-based approaches

  Betül Güvenç Paltun; Hiroshi Mamitsuka; Samuel Kaski

  Briefings in Bioinformatics, 18 Jan. 2021, Peer-reviewed

  
• A survey on adverse drug reaction studies: data, tasks and machine learning methods

  Duc Anh Nguyen; Canh Hao Nguyen; Hiroshi Mamitsuka

  Briefings in Bioinformatics, 18 Jan. 2021, Peer-reviewed, Last author

  
• Eukaryotic virus composition can predict the efficiency of carbon export in the global ocean

  Hiroto Kaneko; Romain Blanc-Mathieu; Hisashi Endo; Samuel Chaffron; Tom O. Delmont; Morgan Gaia; Nicolas Henry; Rodrigo Hernández-Velázquez; Canh Hao Nguyen; Hiroshi Mamitsuka; Patrick Forterre; Olivier Jaillon; Colomban de Vargas; Matthew B. Sullivan; Curtis A. Suttle; Lionel Guidi; Hiroyuki Ogata

  iScience, Jan. 2021, Peer-reviewed

  
• Machine Learning for Metabolic Identification

  Dai Hai Nguyen; Canh Hao Nguyen; Hiroshi Mamitsuka

  Creative Complex Systems, 2021, Invited, Last author

  
• DIVERSE: Bayesian Data IntegratiVE Learning for Precise Drug ResponSE Prediction

  Betul Guvenc Paltun; Samuel Kaski; Hiroshi Mamitsuka

  IEEE/ACM Transactions on Computational Biology and Bioinformatics, 01 Jul. 2022, Peer-reviewed, Last author

  
• HPOLabeler: improving prediction of human protein–phenotype associations by learning to rank

  Lizhi Liu; Xiaodi Huang; Hiroshi Mamitsuka; Shanfeng Zhu

  Bioinformatics, 30 Jul. 2020, Peer-reviewed

  
• Scalable Probabilistic Matrix Factorization with Graph-Based Priors

  Jonathan Strahl; Jaakko Peltonen; Hirsohi Mamitsuka; Samuel Kaski

  Proceedings of the AAAI Conference on Artificial Intelligence, 03 Apr. 2020, Peer-reviewed

  
• Efficiently Enumerating Substrings with Statistically Significant Frequencies of Locally Optimal Occurrences in Gigantic String

  Atsuyoshi Nakamura; Ichigaku Takigawa; Hiroshi Mamitsuka

  Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2020), 03 Apr. 2020, Peer-reviewed

  
• FullMeSH: Improving large-scale MeSH indexing with full text

  Suyang Dai; Ronghui You; Zhiyong Lu; Xiaodi Huang; Hiroshi Mamitsuka; Shanfeng Zhu

  Bioinformatics, 01 Mar. 2020, Peer-reviewed

  
• Scaled coupled norms and coupled higher-order tensor completion

  Kishan Wimalawarne; Makoto Yamada; Hiroshi Mamitsuka

  Neural Computation, 01 Feb. 2020, Peer-reviewed

  
• Learning on Hypergraphs with Sparsity

  Hao Canh Nguyen; Hiroshi Mamitsuka

  IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, Peer-reviewed

  
• HPOAnnotator: improving large-scale prediction of HPO annotations by low-rank approximation with HPO semantic similarities and multiple PPI networks

  Junning Gao; Lizhi Liu; Shuwei Yao; Xiaodi Huang; Hiroshi Mamitsuka; Shanfeng Zhu

  BMC Medical Genomics, 23 Dec. 2019, Peer-reviewed

  
• Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

  Michael P. Menden; Dennis Wang; Mike J. Mason; Bence Szalai; Krishna C. Bulusu; Yuanfang Guan; Thomas Yu; Jaewoo Kang; Minji Jeon; Russ Wolfinger; Tin Nguyen; Mikhail Zaslavskiy; Jordi Abante; Barbara Schmitz Abecassis; Nanne Aben; Delasa Aghamirzaie; Tero Aittokallio; Farida S. Akhtari; Bissan Al-lazikani; Tanvir Alam; Amin Allam; Chad Allen; Mariana Pelicano de Almeida; Doaa Altarawy; Vinicius Alves; Alicia Amadoz; Benedict Anchang; Albert A. Antolin; Jeremy R. Ash; Victoria Romeo Aznar; Wail Ba-alawi; Moeen Bagheri; Vladimir Bajic; Gordon Ball; Pedro J. Ballester; Delora Baptista; Christopher Bare; Mathilde Bateson; Andreas Bender; Denis Bertrand; Bhagya Wijayawardena; Keith A. Boroevich; Evert Bosdriesz; Salim Bougouffa; Gergana Bounova; Thomas Brouwer; Barbara Bryant; Manuel Calaza; Alberto Calderone; Stefano Calza; Stephen Capuzzi; Jose Carbonell-Caballero; Daniel Carlin; Hannah Carter; Luisa Castagnoli; Remzi Celebi; Gianni Cesareni; Hyeokyoon Chang; Guocai Chen; Haoran Chen; Huiyuan Chen; Lijun Cheng; Ariel Chernomoretz; Davide Chicco; Kwang Hyun Cho; Sunghwan Cho; Daeseon Choi; Jaejoon Choi; Kwanghun Choi; Minsoo Choi; Martine De Cock; Elizabeth Coker; Isidro Cortes-Ciriano; Miklós Cserzö; Cankut Cubuk; Christina Curtis; Dries Van Daele; Cuong C. Dang; Tjeerd Dijkstra; Joaquin Dopazo; Sorin Draghici; Anastasios Drosou; Michel Dumontier; Friederike Ehrhart; Fatma Elzahraa Eid; Mahmoud ElHefnawi; Haitham Elmarakeby; Bo van Engelen; Hatice Billur Engin; Iwan de Esch; Chris Evelo; Andre O. Falcao; Sherif Farag; Carlos Fernandez-Lozano; Kathleen Fisch; Asmund Flobak; Chiara Fornari; Amir B.K. Foroushani; Donatien Chedom Fotso; Denis Fourches

  Nature Communications, 01 Dec. 2019, Peer-reviewed

  
• AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification.

  Ronghui You; Zihan Zhang; Ziye Wang; Suyang Dai; Hiroshi Mamitsuka; Shanfeng Zhu

  Proceedings of the 33rd Annual Conference on Neural Information Processing Systems (NeurIPS 2019), Dec. 2019, Peer-reviewed

  
• Modelling G×E with historical weather information improves genomic prediction in new environments

  Gillberg Jussi; Marttinen Pekka; Mamitsuka Hiroshi; Kaski Samuel

  Bioinformatics, Oct. 2019, Peer-reviewed

  
• Multiplicative Sparse Feature Decomposition for Efficient Multi-View Multi-Task Learning

  Lu Sun; Canh Hao Nguyen; Hiroshi Mamitsuka

  Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Aug. 2019, Peer-reviewed

  
• Fast and Robust Multi-View Multi-Task Learning via Group Sparsity

  Sun Lu; Nguyen Canh Hao; Mamitsuka Hiroshi

  Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI 2019), Aug. 2019, Peer-reviewed

  
• Viruses of the eukaryotic plankton are predicted to increase carbon export efficiency in the global sunlit ocean

  Romain Blanc-Mathieu; Hiroto Kaneko; Hisashi Endo; Samuel Chaffron; Rodrigo Hern{\'{a; ndez-Vel{\'{a } }zquez; Canh Hao Nguyen; Hiroshi Mamitsuka; Nicolas Henry; Colomban de Vargas; Matthew B. Sullivan; Curtis A. Suttle; Lionel Guidi; Hiroyuki Ogata

  Jul. 2019, Peer-reviewed

  
• A Metropolis-Hastings Sampling of Subtrees in Graphs

  Eid Abdelrahman Munther; Mamitsuka Hiroshi; Wicker Nicolas

  Austrian Journal of Statistics, Jul. 2019, Peer-reviewed

  
• A p-Laplacian Random Walk: Application to Video Games

  Wicker Nicolas; Nguyen Canh Hao; Mamitsuka Hiroshi

  Austrian Journal of Statistics, Jul. 2019, Peer-reviewed

  
• NetGO: improving large-scale protein function prediction with massive network information

  You Ronghui; Yao Shuwei; Xiong Yi; Huang Xiaodi; Sun Fengzhu; Mamitsuka Hiroshi; Zhu Shanfeng

  Nucleic Acids Research, Jul. 2019, Peer-reviewed

  
• ADAPTIVE: leArning DAta-dePendenT, concIse molecular VEctors for fast, accurate metabolite identification from tandem mass spectra

  Nguyen Dai Hai; Nguyen Canh Hao; Mamitsuka Hiroshi

  Bioinformatics (Proceedings of the 27th International Conference on Intelligent Systems for Molecular Biology (ISMB/ECCB 2019)), Jul. 2019, Peer-reviewed

  
• Machine Learning for Marketing

  Hiroshi Mamitsuka

  Jun. 2019

  
• Editorial

  Shuigeng Zhou; Yi-Ping Phoebe Chen; Hiroshi Mamitsuka

  IEEE/ACM Transactions on Computational Biology and Bioinformatics, Mar. 2019, Peer-reviewed

  
• CalCleaveMKL: a Tool for Calpain Cleavage Prediction.

  duVerle DA; Mamitsuka H

  Methods in molecular biology (Clifton, N.J.), 2019, Peer-reviewed

  
• Textbook of Machine Learning and Data Mining (with Bioinformatics Applications)

  Hiroshi Mamitsuka

  Global Data Science Publishing, Sep. 2018, Peer-reviewed

  
• Recent Advances and Prospects of Computational Methods for Metabolite Identification: A Review with Emphasis on Machine Learning Approaches

  Dai Hai Nguyen; Canh Hao Nguyen; Hiroshi Mamitsuka

  Briefings in Bioinformatics, Aug. 2018, Peer-reviewed

  
• Convex Coupled Matrix and Tensor Completion

  Kishan Wimalawarne; Makoto Yamada; Hiroshi Mamitsuka

  Neural Computation, Aug. 2018, Peer-reviewed

  
• Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data

  Makoto Yamada; Jiliang Tang; Jose Lugo-Martinez; Ermin Hodzic; Raunak Shrestha; Avishek Saha; Hua Ouyang; Dawei Yin; Hiroshi Mamitsuka; Cenk Sahinalp; Predrag Radivojac; Filippo Menczer; Yi Chang

  IEEE Transactions on Knowledge and Data Engineering, 01 Jul. 2018, Peer-reviewed

  
• SIMPLE: Sparse Interaction Model over Peaks of moLEcules for fast, interpretable metabolite identification from tandem mass spectra

  Dai Hai Nguyen; Canh Hao Nguyen; Mamitsuka Hiroshi

  Bioinformatics (Proceedings of the 26th International Conference on Intelligent Systems for Molecular Biology (ISMB 2018)), Jun. 2018, Peer-reviewed

  
• GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank

  You Ronghui; Zhang Zihan; Xiong Yi; Sun Fengzhu; Mamitsuka Hiroshi; Zhu Shanfeng

  Bioinformatics, Mar. 2018, Peer-reviewed

  
• AiProAnnotator: Low-rank Approximation with network side information for high-performance, large-scale human Protein abnormality Annotator

  Gao Junning; Yao Shuwei; Mamitsuka Hiroshi; Zhu Shanfeng

  PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2018, Peer-reviewed

  
• Efficient Convex Completion of Coupled Tensors using Coupled Nuclear Norms

  Wimalawarne Kishan; Mamitsuka Hiroshi

  Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada., 2018, Peer-reviewed

  
• Factor Analysis on a Graph.

  Masayuki Karasuyama; Hiroshi Mamitsuka

  International Conference on Artificial Intelligence and Statistics, AISTATS 2018, 9-11 April 2018, Playa Blanca, Lanzarote, Canary Islands, Spain, 2018, Peer-reviewed

  
• SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining

  Kei-ichiro Takahashi; David A. duVerle; Sohiya Yotsukura; Ichigaku Takigawa; Hiroshi Mamitsuka

  Methods in Molecular Biology, 2018, Peer-reviewed

  
• DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank

  Jieyao Deng; Qingjun Yuan; Hiroshi Mamitsuka; Shanfeng Zhu

  Methods in Molecular Biology, 2018, Peer-reviewed

  
• MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing

  Shengwen Peng; Hiroshi Mamitsuka; Shanfeng Zhu

  Methods in Molecular Biology, 2018, Peer-reviewed

  
• Data Mining for Systems Biology

  Hiroshi Mamitsuka

  Methods in Molecular Biology, 2018, Peer-reviewed

  
• Preface

  Mamitsuka H

  Methods in Molecular Biology, 2018, Peer-reviewed

  
• A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines

  Mehmet Gonen; Barbara A. Weir; Glenn S. Cowley; Francisca Vazquez; Yuanfang Guan; Alok Jaiswal; Masayuki Karasuyama; Vladislav Uzunangelov; Tao Wang; Aviad Tsherniak; Sara Howell; Daniel Marbach; Bruce Hoff; Thea C. Norman; Antti Airola; Adrian Bivol; Kerstin Bunte; Daniel Carlin; Sahil Chopra; Alden Deran; Kyle Ellrott; Peddinti Gopalacharyulu; Kiley Graim; Samuel Kaski; Suleiman A. Khan; Yulia Newton; Sam Ng; Tapio Pahikkala; Evan Paull; Artem Sokolov; Hao Tang; Jing Tang; Krister Wennerberg; Yang Xie; Xiaowei Zhan; Fan Zhu; Tero Aittokallio; Hiroshi Mamitsuka; Joshua M. Stuart; Jesse S. Boehm; David E. Root; Guanghua Xiao; Gustavo Stolovitzky; William C. Hahn; Adam A. Margolin

  CELL SYSTEMS, Nov. 2017, Peer-reviewed

  
• Convex factorization machine for toxicogenomics prediction

  Makoto Yamada; Wenzhao Lian; Amit Goyal; Jianhui Chen; Kishan Wimalawarne; Suleiman A. Khan; Samuel Kaski; Hiroshi Mamitsuka; Yi Chang

  Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13 Aug. 2017, Peer-reviewed

  
• Exploring phenotype patterns of breast cancer within somatic mutations: A modicum in the intrinsic code

  Sohiya Yotsukura; Masayuki Karasuyama; Ichigaku Takigawa; Hiroshi Mamitsuka

  Briefings in Bioinformatics, 01 Jul. 2017, Peer-reviewed

  
• Exploring phenotype patterns of breast cancer within somatic mutations: a modicum in the intrinsic code

  Sohiya Yotsukura; Masayuki Karasuyama; Ichigaku Takigawa; Hiroshi Mamitsuka

  BRIEFINGS IN BIOINFORMATICS, Jul. 2017, Peer-reviewed

  
• Adaptive edge weighting for graph-based learning algorithms

  Masayuki Karasuyama; Hiroshi Mamitsuka

  MACHINE LEARNING, Feb. 2017, Peer-reviewed

  
• Generalized Sparse Learning of Linear Models Over the Complete Subgraph Feature Set

  Ichigaku Takigawa; Hiroshi Mamitsuka

  IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Feb. 2017, Peer-reviewed

  
• Computational recognition for long non-coding RNA (lncRNA): Software and databases

  Sohiya Yotsukura; David duverle; Timothy Hancock; Yayoi Natsume-Kitatani; Hiroshi Mamitsuka

  BRIEFINGS IN BIOINFORMATICS, Jan. 2017, Peer-reviewed

  
• Introduction to the special issue on GIW 2016

  Shuigeng Zhou; Phoebe Yi-Ping Chen; Hiroshi Mamitsuka

  Journal of Bioinformatics and Computational Biology, Oct. 2016, Peer-reviewed

  
• GENE-PROXIMITY MODELS FOR GENOME-WIDE ASSOCIATION STUDIES

  Ian Johnston; Timothy Hancock; Hiroshi Mamitsuka; Luis Carvalho

  ANNALS OF APPLIED STATISTICS, Sep. 2016, Peer-reviewed

  
• A Robust Convex Formulations for Ensemble Clustering.

  Gao, J; Yamada, M; Kaski, S; Mamitsuka, H; Zhu, S

  Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), Jul. 2016, Peer-reviewed

  
• NMRPro: an integrated web component for interactive processing and visualization of NMR spectra

  Ahmed Mohamed; Canh Hao Nguyen; Hiroshi Mamitsuka

  BIOINFORMATICS, Jul. 2016, Peer-reviewed

  
• DeepMeSH: deep semantic representation for improving large-scale MeSH indexing

  Shengwen Peng; Ronghui You; Hongning Wang; Chengxiang Zhai; Hiroshi Mamitsuka; Shanfeng Zhu

  BIOINFORMATICS, Jun. 2016, Peer-reviewed

  
• DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank

  Qingjun Yuan; Junning Gao; Dongliang Wu; Shihua Zhang; Hiroshi Mamitsuka; Shanfeng Zhu

  BIOINFORMATICS, Jun. 2016, Peer-reviewed

  
• New Resistance Distances with Global Information on Large Graphs.

  Nguyen, C. H; Mamitsuka, H

  JMLR Workshop and Conference Proceedings (Proceedings of the Nineteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2016)), May 2016, Peer-reviewed

  
• MetaMHCpan, A Meta Apporach for Pan-specific MHC Peptide Binding Prediction.

  Xu, Y; Luo, C; Mamitsuka, H; Zhu, S

  Methods in Molecular Biology, Apr. 2016, Peer-reviewed

  
• Predictions of Cleavability of Calpain Proteolysis by Quantitative Structure-Activity Relationship Analysis Using Newly Determined Cleavage Sites and Catalytic Efficiencies of an Oligopeptide Array

  Fumiko Shinkai-Ouchi; Suguru Koyama; Yasuko Ono; Shoji Hata; Koichi Ojima; Mayumi Shindo; David duVerle; Mika Ueno; Fujiko Kitamura; Naoko Doi; Ichigaku Takigawa; Hiroshi Mamitsuka; Hiroyuki Sorimachi

  MOLECULAR & CELLULAR PROTEOMICS, Apr. 2016, Peer-reviewed

  
• Classification of Promoters Based on the Combination of Core Promoter Elements Exhibits Different Histone Modification Patterns

  Yayoi Natsume-Kitatani; Hiroshi Mamitsuka

  PLOS ONE, Mar. 2016, Peer-reviewed

  
• Current status and prospects of computational resources for natural product dereplication: a review

  Ahmed Mohamed; Canh Hao Nguyen; Hiroshi Mamitsuka

  BRIEFINGS IN BIOINFORMATICS, Mar. 2016, Peer-reviewed

  
• Mining approximate patterns with frequent locally optimal occurrences

  Atsuyoshi Nakamura; Ichigaku Takigawa; Hisashi Tosaka; Mineichi Kudo; Hiroshi Mamitsuka

  DISCRETE APPLIED MATHEMATICS, Feb. 2016, Peer-reviewed

  
• A bioinformatics approach for understanding genotype-phenotype correlation in breast cancer

  Sohiya Yotsukura; Masayuki Karasuyama; Ichigaku Takigawa; Hiroshi Mamitsuka

  Big Data Analytics in Genomics, 01 Jan. 2016, Peer-reviewed

  
• Some Properties of a Dissimilarity Measure for Labeled Graphs.

  Wicker, N; Nguyen, C. H; Mamitsuka, H

  Publications Mathématiques de Besançon: Algèbre et Théorie des Nombres, 2016, Peer-reviewed

  
• MeSHSim: An R/Bioconductor package for measuring semantic similarity over MeSH headings and MEDLINE documents

  Jing Zhou; Yuxuan Shui; Shengwen Peng; Xuhui Li; Hiroshi Mamitsuka; Shanfeng Zhu

  JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY, Dec. 2015, Peer-reviewed

  
• BMExpert: Mining MEDLINE for Finding Experts in Biomedical Domains Based on Language Model

  Beichen Wang; Xiaodong Chen; Hiroshi Mamitsuka; Shanfeng Zhu

  IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, Nov. 2015, Peer-reviewed

  
• 機械学習による薬物分子-ターゲット相互作用予測

  馬見塚 拓

  SAR News, Oct. 2015, Invited

  
• Instance-wise Weighted Nonnegative Matrix Factorization for Aggregating Partitions with Locally Reliable Clusters.

  Zheng, X; Zhu, S; Gao, J; Mamitsuka, H

  Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), Jul. 2015, Peer-reviewed

  
• In Silico Predictions of Human Skin Permeability using Nonlinear Quantitative Structure-Property Relationship Models

  Hiromi Baba; Jun-ichi Takahara; Hiroshi Mamitsuka

  PHARMACEUTICAL RESEARCH, Jul. 2015, Peer-reviewed

  
• MeSHLabeler: improving the accuracy of large-scale MeSH indexing by integrating diverse evidence

  Ke Liu; Shengwen Peng; Junqiu Wu; Chengxiang Zhai; Hiroshi Mamitsuka; Shanfeng Zhu

  BIOINFORMATICS, Jun. 2015, Peer-reviewed

  
• Non-Negative Matrix Factorization with Auxiliary Information on Overlapping Groups

  Motoki Shiga; Hiroshi Mamitsuka

  IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, Jun. 2015, Peer-reviewed

  
• Evaluation of serum-based cancer biomarkers: A brief review from a clinical and computational viewpoint

  Sohiya Yotsukura; Hiroshi Mamitsuka

  CRITICAL REVIEWS IN ONCOLOGY HEMATOLOGY, Feb. 2015, Peer-reviewed

  
• MeSHSim: An R/Bioconductor package for measuring semantic similarity over MeSH headings and MEDLINE documents

  Zhou Jing; Shui Yuxuan; Peng Shengwen; Li Xuhui; Hiroshi Mamitsuka; Zhu Shanfeng

  2015 34TH CHINESE CONTROL CONFERENCE (CCC), 2015, Peer-reviewed

  
• NetPathMiner: R/Bioconductor package for network path mining through gene expression

  Ahmed Mohamed; Timothy Hancock; Canh Hao Nguyen; Hiroshi Mamitsuka

  BIOINFORMATICS, Nov. 2014, Peer-reviewed

  
• Similarity-based machine learning methods for predicting drug-target interactions: a brief review

  Hao Ding; Ichigaku Takigawa; Hiroshi Mamitsuka; Shanfeng Zhu

  BRIEFINGS IN BIOINFORMATICS, Sep. 2014, Peer-reviewed

  
• Selecting Graph Cut Solutions via Global Graph Similarity

  Canh Hao Nguyen; Nicolas Wicker; Hiroshi Mamitsuka

  IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Jul. 2014, Peer-reviewed

  
• Detecting Differentially Coexpressed Genes from Labeled Expression Data: A Brief Review

  Mitsunori Kayano; Motoki Shiga; Hiroshi Mamitsuka

  IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, Jan. 2014, Peer-reviewed

  
• SiBIC: A web server for generating gene set networks based on biclusters obtained by maximal frequent itemset mining

  Kei-Ichiro Takahashi; Ichigaku Takigawa; Hiroshi Mamitsuka

  PLoS ONE, 30 Dec. 2013, Peer-reviewed

  
• Multiple Graph Label Propagation by Sparse Integration

  Masayuki Karasuyama; Hiroshi Mamitsuka

  IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Dec. 2013, Peer-reviewed

  
• Manifold-based Similarity Adaptation for Label Propagation.

  Karasuyama, M; Mamitsuka, H

  Proceedings of the Twenty-Seventh Annual Conference on Neural Information Processing Systems (NIPS 2013), Dec. 2013, Peer-reviewed

  
• Collaborative matrix factorization with multiple similarities for predicting drug-Target interactions

  Xiaodong Zheng; Hao Ding; Hiroshi Mamitsuka; Shanfeng Zhu

  Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 11 Aug. 2013, Peer-reviewed

  
• Efficient Semisupervised MEDLINE Document Clustering With MeSH-Semantic and Global-Content Constraints

  Jun Gu; Wei Feng; Jia Zeng; Hiroshi Mamitsuka; Shanfeng Zhu

  IEEE TRANSACTIONS ON CYBERNETICS, Aug. 2013, Peer-reviewed

  
• Fast algorithms for finding a minimum repetition representation of strings and trees

  Atsuyoshi Nakamura; Tomoya Saito; Ichigaku Takigawa; Mineichi Kudo; Hiroshi Mamitsuka

  Discrete Applied Mathematics, Jul. 2013, Peer-reviewed

  
• A new dissimilarity measure for comparing labeled graphs

  Nicolas Wicker; Canh Hao Nguyen; Hiroshi Mamitsuka

  LINEAR ALGEBRA AND ITS APPLICATIONS, Mar. 2013, Peer-reviewed

  
• Graph mining: procedure, application to drug discovery and recent advances

  Ichigaku Takigawa; Hiroshi Mamitsuka

  DRUG DISCOVERY TODAY, Jan. 2013, Peer-reviewed, Invited

  
• Integrated View of the Human Chromosome X-centric Proteome Project

  Tadashi Yamamoto; Keiichi Nakayama; Hisashi Hirano; Takeshi Tomonaga; Yasushi Ishihama; Tetsushi Yamada; Tadashi Kondo; Yoshio Kodera; Yuichi Satop; None Araki; Hiroshi Mamitsuka; Naoki Goshima

  JOURNAL OF PROTEOME RESEARCH, Jan. 2013, Peer-reviewed

  
• Identifying pathways of coordinated gene expression.

  Hancock T; Takigawa I; Mamitsuka H

  Methods in molecular biology (Clifton, N.J.), 2013, Peer-reviewed

  
• An in silico model for interpreting polypharmacology in drug-target networks.

  Takigawa I; Tsuda K; Mamitsuka H

  Methods in molecular biology (Clifton, N.J.), 2013, Peer-reviewed

  
• Variational Bayes co-clustering with auxiliary information

  Motoki Shiga; Hiroshi Mamitsuka

  MultiClust 2013 - 4th Workshop on Multiple Clusterings, Multi-View Data, and Multi-Source Knowledge-Driven Clustering, in Conj. with the 19th ACM SIGKDD Int. Conf. on KDD 2013, 2013, Peer-reviewed

  
• Machine Learning Sequence Classification Techniques: Application to Cysteine Protease Cleavage Prediction

  David A. duVerle; Hiroshi Mamitsuka

  CURRENT BIOINFORMATICS, Dec. 2012, Peer-reviewed

  
• Latent Feature Kernels for Link Prediction on Sparse Graphs

  Canh Hao Nguyen; Hiroshi Mamitsuka

  IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Nov. 2012, Peer-reviewed

  
• Boosted Network Classifiers for Local Feature Selection

  Timothy Hancock; Hiroshi Mamitsuka

  IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, Nov. 2012, Peer-reviewed

  
• Mining from protein-protein interactions

  Hiroshi Mamitsuka

  WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY, Sep. 2012, Peer-reviewed, Invited

  
• Understanding the substrate specificity of conventional calpains

  Hiroyuki Sorimachi; Hiroshi Mamitsuka; Yasuko Ono

  BIOLOGICAL CHEMISTRY, Sep. 2012, Peer-reviewed, Invited

  
• Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and tools

  Lianming Zhang; Keiko Udaka; Hiroshi Mamitsuka; Shanfeng Zhu

  BRIEFINGS IN BIOINFORMATICS, May 2012, Peer-reviewed

  
• A review of statistical methods for prediction of proteolytic cleavage

  David A. duVerle; Hiroshi Mamitsuka

  BRIEFINGS IN BIOINFORMATICS, May 2012, Peer-reviewed

  
• Close up実験法 Series225 ROS-DETによる遺伝子「スイッチ発現」検出

  茅野 光範; 馬見塚 拓

  実験医学, Apr. 2012, Invited

  
• A Variational Bayesian Framework for Clustering with Multiple Graphs

  Motoki Shiga; Hiroshi Mamitsuka

  IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, Apr. 2012, Peer-reviewed

  
• Efficient semi-supervised learning on locally informative multiple graphs

  Motoki Shiga; Hiroshi Mamitsuka

  PATTERN RECOGNITION, Mar. 2012, Peer-reviewed

  
• TEPITOPEpan: Extending TEPITOPE for Peptide Binding Prediction Covering over 700 HLA-DR Molecules

  Lianming Zhang; Yiqing Chen; Hau-San Wong; Shuigeng Zhou; Hiroshi Mamitsuka; Shanfeng Zhu

  PLOS ONE, Feb. 2012, Peer-reviewed

  
• Identifying Neighborhoods of Coordinated Gene Expression and Metabolite Profiles

  Timothy Hancock; Nicolas Wicker; Ichigaku Takigawa; Hiroshi Mamitsuka

  PLOS ONE, Feb. 2012, Peer-reviewed

  
• Ensemble approaches for improving HLA Class I-peptide binding prediction

  Xihao Hu; Hiroshi Mamitsuka; Shanfeng Zhu

  JOURNAL OF IMMUNOLOGICAL METHODS, Nov. 2011, Peer-reviewed

  
• Clustering genes with expression and beyond

  Motoki Shiga; Hiroshi Mamitsuka

  WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY, Nov. 2011, Peer-reviewed, Invited

  
• Discriminative Graph Embedding for Label Propagation

  Canh Hao Nguyen; Hiroshi Mamitsuka

  IEEE TRANSACTIONS ON NEURAL NETWORKS, Sep. 2011, Peer-reviewed

  
• 化学とグラフアルゴリズム

  瀧川 一学; 馬見塚 拓

  化学と教育, Sep. 2011, Peer-reviewed, Invited

  
• Genome-Wide Integration on Transcription Factors, Histone Acetylation and Gene Expression Reveals Genes Co-Regulated by Histone Modification Patterns

  Yayoi Natsume-Kitatani; Motoki Shiga; Hiroshi Mamitsuka

  PLOS ONE, Jul. 2011, Peer-reviewed

  
• ROS-DET: robust detector of switching mechanisms in gene expression

  Mitsunori Kayano; Ichigaku Takigawa; Motoki Shiga; Koji Tsuda; Hiroshi Mamitsuka

  NUCLEIC ACIDS RESEARCH, Jun. 2011, Peer-reviewed

  
• Calpain Cleavage Prediction Using Multiple Kernel Learning

  David A. duVerle; Yasuko Ono; Hiroyuki Sorimachi; Hiroshi Mamitsuka

  PLOS ONE, May 2011, Peer-reviewed

  
• Mining Significant Substructure Pairs for Interpreting Polypharmacology in Drug-Target Network

  Ichigaku Takigawa; Koji Tsuda; Hiroshi Mamitsuka

  PLOS ONE, Feb. 2011, Peer-reviewed

  
• Efficiently mining delta-tolerance closed frequent subgraphs

  Ichigaku Takigawa; Hiroshi Mamitsuka

  MACHINE LEARNING, Feb. 2011, Peer-reviewed

  
• A spectral approach to clustering numerical vectors as nodes in a network

  Motoki Shiga; Ichigaku Takigawa; Hiroshi Mamitsuka

  PATTERN RECOGNITION, Feb. 2011, Peer-reviewed

  
• Mining Metabolic Network through Gene Expression

  Mamitsuka Hiroshi

  Abstracts for Annual Meeting of Japanese Proteomics Society, 2011

  
• Kernels for Link Prediction with Latent Feature Models

  Canh Hao Nguyen; Hiroshi Mamitsuka

  MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT II, 2011, Peer-reviewed

  
• Glycoinformatics: Data Mining-based Approaches

  Hiroshi Mamitsuka

  CHIMIA, 2011, Invited

  
• Mining Patterns from Glycan Structures.

  Takigawa, I; Hashimoto, K; Shiga, M; Kanehisa, M; Mamitsuka, H

  Proceedings of the International Beilstein Symposium on Glyco-Bioinformatics, Dec. 2010

  
• On network-based kernel methods for protein-protein interactions with applications in protein functions prediction

  Limin Li; Waiki Ching; Yatming Chan; Hiroshi Mamitsuka

  JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY, Oct. 2010, Peer-reviewed

  
• Mining metabolic pathways through gene expression

  Timothy Hancock; Ichigaku Takigawa; Hiroshi Mamitsuka

  BIOINFORMATICS, Sep. 2010, Peer-reviewed

  
• MetaMHC: a meta approach to predict peptides binding to MHC molecules

  Xihao Hu; Wenjian Zhou; Keiko Udaka; Hiroshi Mamitsuka; Shanfeng Zhu

  NUCLEIC ACIDS RESEARCH, Jul. 2010, Peer-reviewed

  
• On the Performance of Methods for Finding a Switching Mechanism in Gene Expression.

  Kayano, M; Takigawa, I; Shiga, M; Tsuda, K; Mamitsuka, H

  Genome Informatics (Proceedings of the Tenth Annual Workshop on Bioinformatics and Systems Biology (IBSB 2010)), Jul. 2010, Peer-reviewed

  
• Boosted Optimization for Network Classification.

  Hancock, T; Mamitsuka, H

  JMLR Workshop and Conference Proceedings (Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010)), May 2010, Peer-reviewed

  
• A markov classification model for metabolic pathways

  Timothy Hancock; Hiroshi Mamitsuka

  ALGORITHMS FOR MOLECULAR BIOLOGY, Jan. 2010, Peer-reviewed

  
• TAP Hunter: A SVM-based system for predicting TAP ligands using local description of amino acid sequence

  Tze Hau Lam; Hiroshi Mamitsuka; Ee Chee Ren; Joo Chuan Tong

  Immunome Research, 2010, Peer-reviewed

  
• Algorithms for finding a minimum repetition representation of a string

  Atsuyoshi Nakamura; Tomoya Saito; Ichigaku Takigawa; Hiroshi Mamitsuka; Mineichi Kudo

  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, Peer-reviewed

  
• Variational Bayes learning over multiple graphs

  Motoki Shiga; Hiroshi Mamitsuka

  Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010, 2010, Peer-reviewed

  
• HAMSTER: Visualizing microarray experiments as a set of minimum spanning trees

  Raymond Wan; Larisa Kiseleva; Hajime Harada; Hiroshi Mamitsuka; Paul Horton

  Source Code for Biology and Medicine, 20 Nov. 2009, Peer-reviewed

  
• Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data

  Mitsunori Kayano; Ichigaku Takigawa; Motoki Shiga; Koji Tsuda; Hiroshi Mamitsuka

  BIOINFORMATICS, Nov. 2009, Peer-reviewed

  
• Field independent probabilistic model for clustering multi-field documents

  Shanfeng Zhu; Ichigaku Takigawa; Jia Zeng; Hiroshi Mamitsuka

  INFORMATION PROCESSING & MANAGEMENT, Sep. 2009, Peer-reviewed

  
• Enhancing MEDLINE document clustering by incorporating MeSH semantic similarity

  Shanfeng Zhu; Jia Zeng; Hiroshi Mamitsuka

  BIOINFORMATICS, Aug. 2009, Peer-reviewed

  
• Annotating Gene Functions with Integrative Spectral Clustering on Microarray Expressions and Sequences.

  Li, L; Shiga, M; Ching, W.-K; Mamitsuka, H

  Genome Informatics (Proceedings of the Ninth Annual Workshop on Bioinformatics and Systems Biology (IBSB 2009)), Jul. 2009

  
• CaMPDB: a Resource for Calpain and Modulatory Proteolysis.

  duVerle, D. A; Takigawa, I; Ono, Y; Sorimachi, H; Mamitsuka, H

  Genome Informatics (Proceedings of the Ninth Annual Workshop on Bioinformatics and Systems Biology (IBSB 2009)), Jul. 2009

  
• Active Pathway Identification and Classification with Probabilistic Ensembles.

  Hancock, T; Mamitsuka, H

  Genome Informatics (Proceedings of the Ninth Annual Workshop on Bioinformatics and Systems Biology (IBSB 2009)), Jul. 2009

  
• Efficient Probabilistic Latent Semantic Analysis through Parallelization

  Raymond Wan; Vo Ngoc Anh; Hiroshi Mamitsuka

  INFORMATION RETRIEVAL TECHNOLOGY, PROCEEDINGS, 2009, Peer-reviewed

  
• A Markov Classification Model for Metabolic Pathways

  Timothy Hancock; Hiroshi Mamitsuka

  ALGORITHMS IN BIOINFORMATICS, PROCEEDINGS, 2009, Peer-reviewed

  
• Discovering network motifs in protein interaction networks

  Raymond Wan; Hiroshi Mamitsuka

  Biological Data Mining in Protein Interaction Networks, 2009, Peer-reviewed

  
• A Study of Network-based Kernel Methods on Protein-Protein Interaction for Protein Functions Prediction

  Wai-Ki Ching; Limin Li; Yat-Ming Chan; Hiroshi Mamitsuka

  OPTIMIZATION AND SYSTEMS BIOLOGY, 2009, Peer-reviewed

  
• Mining significant tree patterns in carbohydrate sugar chains

  Kosuke Hashimoto; Ichigaku Takigawa; Motoki Shiga; Minoru Kanehisa; Hiroshi Mamitsuka

  BIOINFORMATICS, Aug. 2008, Peer-reviewed

  
• A Framework for Determining Outlying Microarray Experiments.

  Wan; R. Wheelock, A; Mamitsuka, H

  Genome Informatics (Proceedings of the Eighth Annual Workshop on Bioinformatics and Systems Biology (IBSB 2008)), Jun. 2008

  
• Semi-Supervised Graph Partitioning with Decision Trees.

  Hancock, T; Mamitsuka, H

  Genome Informatics (Proceedings of the Eighth Annual Workshop on Bioinformatics and Systems Biology (IBSB 2008)), Jun. 2008

  
• 多様なゲノムデータの統合的クラスタリング解析(理論/実験技術)

  志賀 元紀; 瀧川 一学; 馬見塚 拓

  生物物理, May 2008, Peer-reviewed, Invited

  
• PURE:PubMed文献検索支援システム

  馬見塚 拓; 米屋 隆

  実験医学, May 2008, Invited

  
• A new efficient probabilistic model for mining labeled ordered trees applied to glycobiology

  Kosuke Hashimoto; Kiyoko Flora Aoki-Kinoshita; Nobuhisa Ueda; Minoru Kanehisa; Hiroshi Mamitsuka

  ACM Transactions on Knowledge Discovery from Data, 01 Mar. 2008, Peer-reviewed

  
• Informatic innovations in glycobiology: relevance to drug discovery

  Hiroshi Mamitsuka

  DRUG DISCOVERY TODAY, Feb. 2008, Peer-reviewed

  
• Probabilistic path ranking based on adjacent pairwise coexpression for metabolic transcripts analysis

  Ichigaku Takigawa; Hiroshi Mamitsuka

  BIOINFORMATICS, Jan. 2008, Peer-reviewed

  
• Identification of endocrine disruptor biodegradation by integration of structure-activity relationship with pathway analysis

  Tadashi Kadowaki; Craig E. Wheelock; Tetsuya Adachi; Taku Kudo; Shinobu Okamoto; Nobuya Tanaka; Koichiro Tonomura; Gozoh Tsujimoto; Hiroshi Mamitsuka; Susumu Goto; Minoru Kanehisa

  ENVIRONMENTAL SCIENCE & TECHNOLOGY, Dec. 2007, Peer-reviewed

  
• Active ensemble learning: Application to data mining and bioinformatics

  Hiroshi Mamitsuka; Naoki Abe

  Systems and Computers in Japan, Oct. 2007, Peer-reviewed

  
• Annotating gene function by combining expression data with a modular gene network

  Motoki Shiga; Ichigaku Takigawa; Hiroshi Mamitsuka

  BIOINFORMATICS, Jul. 2007, Peer-reviewed

  
• A hidden Markov model-based approach for identifying timing differences in gene expression under different experimental factors

  Takashi Yoneya; Hiroshi Mamitsuka

  BIOINFORMATICS, Apr. 2007, Peer-reviewed

  
• Predicting implicit associated cancer genes from OMIM and MEDLINE by a new probabilistic model.

  Shanfeng Zhu; Yasushi Okuno; Gozoh Tsujimoto; Hiroshi Mamitsuka

  BMC Systems Biology, 2007, Peer-reviewed

  
• A probabilistic model for clustering text documents with multiple fields

  Shanfeng Zhu; Ichigaku Takigawa; Shuqin Zhang; Hiroshi Mamitsuka

  ADVANCES IN INFORMATION RETRIEVAL, 2007, Peer-reviewed

  
• Pure: A Pubmed Article Recommendation System Based On Content-Based Filtering

  Takashi Yoneya; Hiroshi Mamitsuka

  GENOME INFORMATICS 2007, VOL 18, 2007, Peer-reviewed

  
• A Spectral Clustering Approach to Optimally Combining Numerical Vectors with a Modular Network

  Motoki Shiga; Ichigaku Takigawa; Hiroshi Mamitsuka

  KDD-2007 PROCEEDINGS OF THE THIRTEENTH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2007, Peer-reviewed

  
• Selecting features in microarray classification using ROC curves

  Hiroshi Mamitsuka

  PATTERN RECOGNITION, Dec. 2006, Peer-reviewed

  
• A New Efficient Probabilistic Model for Mining Labeled Ordered Trees.

  Hashimoto, K; Aoki-Kinoshita, K. F; Ueda, N; Kanehisa, M; Mamitsuka, H

  Proceedings of the Twelfth ACM SIGKDD International Conference On Knowledge Discovery and Data Mining (KDD 2006), Aug. 2006, Peer-reviewed

  
• Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules

  Shanfeng Zhu; Keiko Udaka; John Sidney; Alessandro Sette; Kiyoko F. Aoki-Kinoshita; Hiroshi Mamitsuka

  BIOINFORMATICS, Jul. 2006, Peer-reviewed

  
• ProfilePSTMM: capturing tree-structure motifs in carbohydrate sugar chains

  Kiyoko F. Aoki-Kinoshita; Nobuhisa Ueda; Hiroshi Mamitsuka; Minoru Kanehisa

  BIOINFORMATICS, Jul. 2006, Peer-reviewed

  
• Query-learning-based iterative feature-subset selection for learning from high-dimensional data sets

  H Mamitsuka

  KNOWLEDGE AND INFORMATION SYSTEMS, Jan. 2006, Peer-reviewed

  
• Combining Vector-Space and Word-Based Aspect Models for Passage Retrieval.

  Raymond Wan; Ichigaku Takigawa; Hiroshi Mamitsuka; Vo Ngoc Anh

  Proceedings of the Fifteenth Text REtrieval Conference, TREC 2006, Gaithersburg, Maryland, USA, November 14-17, 2006, 2006, Peer-reviewed

  
• Applying Gaussian distribution-dependent criteria to decision trees for high-dimensional microarray data

  Raymond Wan; Ichigaku Takigawa; Hiroshi Mamitsuka

  DATA MINING AND BIOINFORMATICS, 2006, Peer-reviewed

  
• Application of a New Probabilistic Model for Mining Implicit Associated Cancer Genes from OMIM and Medline.

  Zhu, S; Okuno, Y; Tsujimoto, G; Mamitsuka, H

  Cancer Informatics, 2006, Peer-reviewed

  
• A probabilistic model for mining implicit 'chemical compound-gene' relations from literature

  SF Zhu; Y Okuno; G Tsujimoto; H Mamitsuka

  BIOINFORMATICS, Sep. 2005, Peer-reviewed

  
• Finding the biologically optimal alignment of multiple sequences

  H Mamitsuka

  ARTIFICIAL INTELLIGENCE IN MEDICINE, Sep. 2005, Peer-reviewed

  
• Computational intelligence in solving bioinformatics problems

  KJ Cios; H Mamitsuka; T Nagashima; R Tadeusiewicz

  ARTIFICIAL INTELLIGENCE IN MEDICINE, Sep. 2005

  
• A probabilistic model for mining labeled ordered trees: Capturing patterns in carbohydrate sugar chains

  N Ueda; KF Aoki-Kinoshita; A Yamaguchi; T Akutsu; H Mamitsuka

  IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, Aug. 2005, Peer-reviewed

  
• Mining new protein-protein interactions

  H Mamitsuka

  IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE, May 2005, Peer-reviewed

  
• A score matrix to reveal the hidden links in glycans

  KF Aoki; H Mamitsuka; T Akutsu; M Kanehisa

  BIOINFORMATICS, Apr. 2005, Peer-reviewed

  
• Essential latent knowledge for protein-protein interactions: Analysis by an unsupervised learning approach

  H Mamitsuka

  IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, Apr. 2005, Peer-reviewed

  
• Efficient Unsupervised Mining from Noisy Co-occurrence Data

  Mamitsuka, H

  New Mathematics and Natural Computation, Mar. 2005, Peer-reviewed

  
• Cleaning microarray exprossion data using Markov random fields based on profile similarity

  Raymond Wan; Hiroshi Mamitsuka; Kiyoko F. Aoki

  Proceedings of the ACM Symposium on Applied Computing, 2005, Peer-reviewed

  
• Comprehensive analysis and prediction of synthetic lethality using subcellular locations.

  Takuji Yamada; Shuichi Kawashima; Hiroshi Mamitsuka; Susumu Goto; Minoru Kanehisa

  Genome informatics. International Conference on Genome Informatics, 2005, Peer-reviewed

  
• The evolutionary repertoires of the eukaryotic-type ABC transporters in terms of the phylogeny of ATP-binding domains in eukaryotes and prokaryotes

  Y Igarashi; KF Aoki; H Mamitsuka; K Kuma; M Kanehisa

  MOLECULAR BIOLOGY AND EVOLUTION, Nov. 2004, Peer-reviewed

  
• Finding the maximum common subgraph of a partial k-tree and a graph with a polynomially bounded number of spanning trees

  A Yamaguchi; KF Aoki; H Mamitsuka

  INFORMATION PROCESSING LETTERS, Oct. 2004, Peer-reviewed

  
• Application of a new probabilistic model for recognizing complex patterns in glycans

  Kiyoko F. Aoki; Nobuhisa Ueda; Atsuko Yamaguchi; Minoru Kanehisa; Tatsuya Akutsu; Hiroshi Mamitsuka

  BIOINFORMATICS, Aug. 2004, Peer-reviewed

  
• KCaM (KEGG Carbohydrate Matcher): a software tool for analyzing the structures of carbohydrate sugar chains

  KF Aoki; A Yamaguchi; N Ueda; T Akutsu; H Mamitsuka; S Goto; M Kanehisa

  NUCLEIC ACIDS RESEARCH, Jul. 2004, Peer-reviewed

  
• Managing and analyzing carbohydrate data

  KF Aoki; N Ueda; A Yamaguchi; T Akutsu; M Kanehisa; H Mamitsuka

  SIGMOD RECORD, Jun. 2004, Peer-reviewed

  
• A hierarchical mixture of markov models for finding biologically active metabolic paths using gene expression and protein classes

  Hiroshi Mamitsuka; Yasushi Okuno

  Proceedings - 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004, 2004, Peer-reviewed

  
• A general probabilistic framework for mining labeled ordered trees

  N Ueda; KF Aoki; H Mamitsuka

  PROCEEDINGS OF THE FOURTH SIAM INTERNATIONAL CONFERENCE ON DATA MINING, 2004, Peer-reviewed

  
• Mining Biologically Active Patterns in Metabolic Pathways using Microarray Expression Profiles

  Mamitsuka, H; Okuno, Y; Yamaguchi, A

  ACM SIGKDD Explorations, Dec. 2003, Peer-reviewed

  
• Efficient Tree Matching Methods for Accurate Carbohydrate Database Queries.

  Aoki, K. F; Yamaguchi, A; Okuno, Y; Akutsu, T; Ueda, N; Kanehisa, M; Mamitsuka, H

  Genome Informatics (Proceedings of the Fourteenth International Conference on Genome Informatics), Dec. 2003, Peer-reviewed

  
• Hierarchical Latent Knowledge Analysis for Co-occurrence Data.

  Mamitsuka, H

  Proceedings of the Twenties International Conference on Machine Learning (ICML 2003), Aug. 2003, Peer-reviewed

  
• Prediction of MHC Class I Binding Peptides Using an Ensemble Learning Approach

  Majeux Nicolas; Udaka Keiko; Mamitsuka Hiroshi

  GI, 2003

  
• Statistical Significance of Tree Similarity Scores

  Aoki Kiyoko F.; Yamaguchi Atsuko; Okuno Yasushi; Akutsu Tatsuya; Ueda Nobuhisa; Kanehisa Minoru; Mamitsuka Hiroshi

  GI, 2003

  
• Selective sampling with a hierarchical latent variable model

  H Mamitsuka

  ADVANCES IN INTELLIGENT DATA ANALYSIS V, 2003, Peer-reviewed

  
• Detecting experimental noises in protein-protein interactions with iterative sampling and model-based clustering

  H Mamitsuka

  THIRD IEEE SYMPOSIUM ON BIOINFORMATICS AND BIOENGINEERING - BIBE 2003, PROCEEDINGS, 2003, Peer-reviewed

  
• Efficient unsupervised mining from noisy data sets: application to clustering co-occurrence data

  H Mamitsuka

  PROCEEDINGS OF THE THIRD SIAM INTERNATIONAL CONFERENCE ON DATA MINING, 2003, Peer-reviewed

  
• Finding the maximum common subgraph of a partial k-tree and a graph with a polynomially bounded number of spanning trees

  A Yamaguchi; H Mamitsuka

  ALGORITHMS AND COMPUTATION, PROCEEDINGS, 2003, Peer-reviewed

  
• Empirical evaluation of ensemble feature subset selection methods for learning from a high-dimensional database in drug design

  H Mamitsuka

  THIRD IEEE SYMPOSIUM ON BIOINFORMATICS AND BIOENGINEERING - BIBE 2003, PROCEEDINGS, 2003, Peer-reviewed

  
• Efficient mining from heterogeneous data sets for predicting protein-protein interactions

  H Mamitsuka

  14TH INTERNATIONAL WORKSHOP ON DATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS, 2003, Peer-reviewed

  
• Empirical evaluation of a dynamic experiment design method for prediction of MHC class I-binding peptides

  K Udaka; H Mamitsuka; Y Nakaseko; N Abe

  JOURNAL OF IMMUNOLOGY, Nov. 2002, Peer-reviewed

  
• Iteratively Selecting Feature Subsets for Mining from High-Dimensional Databases.

  Mamitsuka, H

  Lecture Notes in Computer Science (Proceedings of the Sixth European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2002)), Aug. 2002, Peer-reviewed

  
• Active Ensemble Learning : Applications to Data Mining and Bioinformatics

  MAMITSUKA Hiroshi; ABE Naoki

  The Transactions of the Institute of Electronics,Information and Communication Engineers., May 2002, Peer-reviewed, Invited

  
• Prediction of MHC class I binding peptides by a query learning algorithm based on hidden Markov models

  K Udaka; H Mamitsuka; Y Nakaseko; N Abe

  JOURNAL OF BIOLOGICAL PHYSICS, 2002, Peer-reviewed

  
• Efficient Mining from Large Databases by Query Learning.

  Mamitsuka, H; Abe, N

  Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Jun. 2000, Peer-reviewed

  
• Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models

  H Mamitsuka

  PROTEINS-STRUCTURE FUNCTION AND GENETICS, Dec. 1998, Peer-reviewed

  
• Query Learning Strategies Using Boosting and Bagging.

  Abe, N; Mamitsuka, H

  Proceedings of the Fifteenth International Conference on Machine Learning (ICML 98), Jul. 1998, Peer-reviewed

  
• Empirical comparison of competing query learning methods

  N Abe; H Mamitsuka; A Nakamura

  DISCOVERY SCIENCE, 1998, Peer-reviewed

  
• Predicting protein secondary structure using stochastic tree grammars

  N Abe; H Mamitsuka

  MACHINE LEARNING, Nov. 1997, Peer-reviewed

  
• Supervised Learning of Hidden Markov Models for Sequence Discrimination.

  Mamitsuka, H

  Proceeds of the First International Conference on Computational Molecular Biology (RECOMB 97), Jan. 1997, Peer-reviewed

  
• A learning method of hidden Markov models for sequence discrimination

  Hiroshi Mamitsuka

  Journal of Computational Biology, 1996, Peer-reviewed

  
• REPRESENTING INTER-RESIDUE DEPENDENCIES IN PROTEIN SEQUENCES WITH PROBABILISTIC NETWORKS

  H MAMITSUKA

  COMPUTER APPLICATIONS IN THE BIOSCIENCES, Aug. 1995, Peer-reviewed

  
• ALPHA-HELIX REGION PREDICTION WITH STOCHASTIC RULE LEARNING

  H MAMITSUKA; K YAMANISHI

  COMPUTER APPLICATIONS IN THE BIOSCIENCES, Aug. 1995, Peer-reviewed

  
• α-helix region prediction with stochastic rule learning

  Hiroshi Mamitsuka; Kenji Yamanishi

  Bioinformatics, Aug. 1995, Peer-reviewed

  
• Representing inter-residue dependencies in protein sequences with probabilistic networks

  Hiroshi Mamitsuka

  Bioinformatics, Aug. 1995, Peer-reviewed

  
• Learning Hidden Markov Models Using Back-Propagation through Time

  Mamitsuka Hiroshi

  GI, 1995

  
• Predicting Location and Structure of Beta-Sheet Regions Using Stochastic Tree Grammars.

  Mamitsuka, H; Abe, N

  Proceedings of the Second International Conferenceon Intelligent Systems for Molecular Biology (ISMB 94), Aug. 1994, Peer-reviewed

  
• A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars.

  Abe, N; Mamitsuka, H

  Proceedings of the Eleventh International Conference on Machine Learning (ML 94), Jul. 1994, Peer-reviewed

  
• A New Method for Predicting Long-range Interactions between Amino Acid Residues Based-on Homological Correlations

  Mamitsuka Hiroshi

  GI, 1994

  
• Prediction of Beta-Sheet Structures Using Stochastic Tree Grammars

  Mamitsuka Hiroshi; Abe Naoki

  GI, 1994

  
• Protein Alpha-Helix Region Prediction Based on Stochastic Rule Learning.

  Mamitsuka, H; Yamanishi, K

  Proceedings of the Twenty-sixth Annual Hawaii International Conference on System Sciences (HICSS 26), Jan. 1993, Peer-reviewed

  
• Representing Inter-residue Dependencies in Protein Sequences with Probabilistic Networks

  Mamitsuka Hiroshi

  GI, 1993

  
• Protein Secondary Structure Prediction Based on Stochastic Rule Learning.

  Mamitsuka, H; Yamanishi, K

  Lecture Notes in Computer Science (Proceedings of the Third Annual Workshop on Algorithmic Learning Theory (ALT 92)), Oct. 1992, Peer-reviewed

  
• Protein α-Helix Region Prediction Using Stochastic-Rule Learning

  Mamitsuka Hiroshi; Yamanishi Kenji

  GI, 1992

  

Misc.

• カルパインの基質切断部位の予測

  大内史子; 小山傑; 小山傑; 小野弥子; 秦勝志; 尾嶋孝一; 尾嶋孝一; 進藤真由美; DE VERLE David; DE VERLE David; 土井奈穂子; 瀧川一学; 瀧川一学; 馬見塚拓; 反町洋之

  日本病態プロテアーゼ学会学術集会プログラム抄録集, 2016

  
• Clustering Features based on Simultaneous Manifold Learning

  烏山 昌幸; 馬見塚 拓

  電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 26 Nov. 2015

  
• Similarity Adaptation for Label Propagation based on Local Linear Reconstruction

  KARASUYAMA Masayuki; MAMITSUKA Hirhoshi

  電子情報通信学会技術研究報告 : 信学技報, 04 Mar. 2013

  
• カルパインの特性を規定する基質特異性の定量的構造-活性相関解析

  大内史子; 小山傑; 小山傑; 進藤真由美; 馬見塚拓; 瀧川一学; 尾嶋孝一; 秦勝志; 小野弥子; 反町洋之

  日本農芸化学会大会講演要旨集(Web), 2017

  
• Propagating Labels via Sparse Combination of Multiple Graphs

  KARASUYAMA Masayuki; MAMITSUKA Hiroshi

  電子情報通信学会技術研究報告. IBISML, 情報論的学習理論と機械学習 = IEICE technical report. IBISML, Information-based induction sciences and machine learning, 31 Oct. 2012

  
• Finding Approximate Frequent Patterns from DNA Sequences

  NAKAMURA Atsuyoshi; TAKIGAWA Ichigaku; TOSAKA Hisashi; KUDO Mineichi; MAMITSUKA Hiroshi

  人工知能学会人工知能基本問題研究会資料, 26 Jan. 2012

  
• Enumerating Biclusters on Gene Expression Data by Mining Frequent Itemsets

  TAKAHASHI Keiichiro; TAKIGAWA Ichigaku; MAMITSUKA Hiroshi

  情報計算化学生物学会大会予稿集, 08 Nov. 2011

  
• ロバスト相関係数差とP-値による交互作用遺伝子対の効率的検出手法

  茅野光範; 茅野光範; 瀧川一学; 瀧川一学; 志賀元紀; 志賀元紀; 津田宏治; 津田宏治; 馬見塚拓; 馬見塚拓

  統計関連学会連合大会講演報告集, 2010

  
• Parametric summarization of frequent subgraphs for characterizing structural features of bioactive compounds

  TAKIGAWA Ichigaku; MAMITSUKA Hiroshi

  Proc Annu Conf Jpn Soc Bioinform, 2010

  
• Finding three-way gene interactions from transcript and genotype data

  KAYANO Mitsunori; TAKIGAWA Ichigaku; SHIGA Motoki; TSUDA Koji; MAMITSUKA Hiroshi

  Proc Annu Conf Jpn Soc Bioinform, 2010

  
• iTRAQを用いた多重ペプチド鎖切断分析によるカルパイン基質配列特異性の解析

  小山傑; 小山傑; 秦勝志; 小野弥子; 上野美香; 瀧川一学; 馬見塚拓; 阿部啓子; 反町洋之; 反町洋之

  日本農芸化学会大会講演要旨集, 2009

  
• iTRAQを用いた多重ペプチド鎖切断分析によるカルパイン基質配列特異性の解析

  小山傑; 小山傑; 秦勝志; 小野弥子; 尾嶋孝一; 尾嶋孝一; 林智佳子; 林智佳子; 北村ふじ子; 土井菜穂子; 土井菜穂子; 瀧川一学; 松島由典; 阿部啓子; 馬見塚拓; 反町洋之; 反町洋之

  日本蛋白質科学会年会プログラム・要旨集, 2008

  
• 酵素遺伝子の発現情報に基づく効率的な代謝経路ランキング

  瀧川一学; 馬見塚拓

  統計関連学会連合大会講演報告集, 2008

  
• Developing a Substrate Predictor with Sequence information

  MATSUSHIMA Yoshifumi; TAKIGAWA Ichigaku; ONO Yasuko; SORIMACHI Hiroyuki; MAMITSUKA Hiroshi

  Proc Annu Conf Jpn Soc Bioinform, 2008

  
• Association of SNPs with Multiple Genes Using a Nonlinear Regression Model

  KAYANO Mitsunori; TAKIGAWA Ichigaku; SHIGA Motoki; TSUDA Koji; MAMITSUKA Hiroshi

  Proc Annu Conf Jpn Soc Bioinform, 2008

  
• Efficiently finding significant substructural patterns conserved in glycans

  TAKIGAWA Ichigaku; HASHIMOTO Kosuke; SHIGA Motoki; KANEHISA Minoru; MAMITSUKA Hiroshi

  Proc Annu Conf Jpn Soc Bioinform, 2008

  
• Gene Sequence Ranking Based on Expression Profiles for Metabolic Pathway Analysis

  TAKIGAWA ICHIGAKU; MAMITSUKA HIROSHI

  情報処理学会研究報告, 09 Feb. 2006

  
• A profile HMM for tree structures to locate glycan structure profiles

  KF Aoki-Kinoshita; N Ueda; H Mamitsuka; S Goto; M Kanehisa

  GLYCOBIOLOGY, Nov. 2005

  
• Application of a probabilistic tree model for determining profiles in glycan tree structures

  AOKI KINOSHITA Kiyoko F.; UEDA Nobuhisa; MAMITSUKA Hiroshi; GOTO Susumu; KANEHISA Minoru

  IPSJ SIG technical reports, 07 Oct. 2005

  
• Mining literature co-occurrence data using a probabilistic model

  ZHU Shanfeng; OKUNO Yasushi; TSUJIMOTO Gozoh; MAMITSUKA Hiroshi

  IPSJ SIG technical reports, 07 Oct. 2005

  
• プロファイル確率兄弟依存木マルコフモデルを用いた糖鎖モチーフの効率的な検出

  木下聖子; 上田展久; 馬見塚拓; 金久実

  日本糖質学会年会要旨集, 2005

  
• Glycan tree alignment and substitution matrix for finding relationships between glycan linkages

  KF Aoki; M Kanehisa; H Mamitsuka

  GLYCOBIOLOGY, Nov. 2004

  
• Learning personal preference functions using boolean-variable real-valued multivariate polynomials

  Nakamura Atsuyoshi; Mamitsuka Hiroshi; Toba Hiroyasu; Abe Naoki

  全国大会講演論文集, 06 Mar. 1996

  
• Potein Secondary Structure Prediction Using Stochas-tic Tree Grammer Learning

  馬見塚 拓; 安倍 直樹

  全国大会講演論文集, 27 Sep. 1993

  

Presentations

• Hypergraph for predicting adverse drug reaction

  Hiroshi Mamitsuka

  Computer Science Colloquium, 24 May 2023, City University of Hong Kong

  
• Hypergraph for predicting adverse drug reactions

  Hiroshi Mamitsuka

  Seminar, 24 Mar. 2023, University of Malaya

  
• Churn analysis - Machine learning approach -

  Hiroshi Mamitsuka

  Seminar, 21 Mar. 2023, University of Malaya

  
• Hypergraph for predicting adverse drug reactions

  Hiroshi Mamitsuka

  Forest Workshop, 27 Feb. 2023

  
• Hypergraph for predicting adverse drug reaction

  Hiroshi Mamitsuka

  Seminar, 17 Feb. 2023, Hokkaido University

  
• Hypergraph for predicting adverse drug reactions

  Hiroshi Mamitsuka

  Seminar, 19 Oct. 2022, Humboldt University Berlin

  
• Hypergraph for predicting adverse drug reactions

  Hiroshi Mamitsuka

  21st International Conference on Systems Biology (ICSB 2022), 12 Oct. 2022, Invited

  
• Exploring phenotype patterns of breast cancer within somatic mutations

  Hiroshi Mamitsuka

  2nd International Congress of Asian Oncology Society (AOS 2022), 16 Jun. 2022, Invited

  
• Progress and results of AI-CropPro project

  Hiroshi Mamitsuka

  Final Seminar of Novel Applications of Artificial Intelligence in Physical Sciences and Engineering Research (AIPSE) (AIPSE Closing Seminar), 30 Mar. 2022, Academy of Finland, Invited

  
• Label Propagation over Multiple Graphs

  Hiroshi Mamitsuka

  1st International Conference on Computational Science and Data Analytics (COMDATA 2021), 24 Nov. 2021, Invited

  
• バイオインフォマティクスのためのグラフによるデータ統合型機械学習

  馬見塚 拓

  第10回生命医薬情報学連合大会(IIBMP2021)、ワークショップ：人工知能と生命誌に基づく生命医科学のためのバイオインフォマティクス, 28 Sep. 2021, 日本バイオインフォマティクス学会(JSBi)

  
• Machine learning for predicting drug-target interactions

  Hiroshi Mamitsuka

  15th World Congress of Societies of Biological Psychiatry, S-05 | Big biological data, data integration and learning algorithms in deciphering major depression and its treatments, 28 Jun. 2021, World Federation of Societies of Biological Psychiatry, Invited

  
• Graph-based Machine Learning

  Hiroshi Mamitsuka

  Seminar, 15 Apr. 2021, Hokkaido University

  
• Fast and Robust Multi-view Multi-task Learning through Group Sparsity

  Hiroshi Mamitsuka

  Seminar, 13 Feb. 2020, IBM T. J. Watson Research Center

  
• Computational Metabolic Identification from Mass Spectra -- Machine Learning Approach --

  Hiroshi Mamitsuka

  Seminar, 23 Jan. 2020, Humboldt University Berlin

  
• Graph-based Machine Learning

  Hiroshi Mamitsuka

  Seminar, 03 Dec. 2019, University of Malaya

  
• Machine Learning Techniques and Applications: Past, Present and Future

  Hiroshi Mamitsuka

  DSx Conference 2019, 26 Nov. 2019, Perdana University, Invited

  
• Graph-smooth, Data-integrative Machine Learning for Bioinformatics

  Hiroshi Mamitsuka

  Seminar, 20 Aug. 2019, Shenzhen University

  
• Graph-smooth, Data-integrative Machine Learning for Bioinformatics

  Hiroshi Mamitsuka

  Seminar, 28 Jun. 2019, Perdana University, Invited

  
• Similarity-based Machine Learning Approaches for Predicting Drug-Target Interactions

  Hiroshi Mamitsuka

  Seminar, 26 Jun. 2019, University of Malaya, Invited

  
• Graph-regularized, Data-integrative Machine Learning for Bioinformatics

  Hiroshi Mamitsuka

  Seminar, 21 Jun. 2019, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Invited

  
• Graph-based Machine Learning

  Hiroshi Mamitsuka

  Seminar, 20 Jun. 2019, Fudan University, Invited

  
• Graph-based Machine Learning

  Hiroshi Mamitsuka

  Workshop on Bioinformatics and Data Analysis, 14 Jun. 2019, Xi'an Jiao Tong University, Invited

  
• Graph-based Machine Learning

  Hiroshi Mamitsuka

  Seminar, 10 May 2019, Kyushu Institute of Technology

  
• Machine Learning for Biological Sciences

  馬見塚 拓

  セミナー, 30 Nov. 2018, 国立国際医療研究センター研究所, Invited

  
• 機械学習による生物科学応用

  馬見塚 拓

  第1245回生物科学セミナー, 28 Nov. 2018, 東京大学理学部生物科学科, Invited

  
• Graph-regularized, Data-integrative Machine Learning for Bioinformatics

  Hiroshi Mamitsuka

  Seminar, 17 Nov. 2018, Ho Chi Minh City National University of Science, Invited

  
• Graph-based Machine Learning

  Hiroshi Mamitsuka

  5th SHTP Annual International Conference 2018, 16 Nov. 2018, Invited

  
• Database, prediction and beyond

  馬見塚 拓

  反町洋之博士 追悼記念シンポジウム~Three Decades of Calpain~, 30 Jul. 2018, Invited

  
• Data Integrative Machine Learning for Bioinformatics

  Hiroshi Mamitsuka

  Second Belgrade BioInformatics Conference (BelBi 2018), 21 Jun. 2018, Invited

  
• Label Propagation over Multiple Graphs

  Hiroshi Mamitsuka

  Seminar, 22 Mar. 2018, ThuyLoi University, Invited

  
• Applying "Learning to Rank" to Large-scale MeSH Indexing

  Hiroshi Mamitsuka

  Seminar, 20 Mar. 2018, Hanoi University of Science and Technology, Invited

  
• Similarly-based Machine Learning Approaches for Predicting Drug-target Interactions

  Hiroshi Mamitsuka

  Sixth International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2018), 16 Mar. 2018, Invited

  
• Collaborative Matrix Factorization for Predicting Drug-Target Interactions

  Hiroshi Mamitsuka

  Internal Seminar, 20 Apr. 2017, Keio University

  
• Learning to Rank: Applications to Bioinformatics

  Hiroshi Mamitsuka

  Seminar, Machine Learning Coffee Series, 27 Mar. 2017, University of Helsinki and Aalto University, Invited

  
• 機械学習・データマイニング技術／応用の過去・現在 そして未来

  馬見塚 拓

  人工知能学会 第102回人工知能基本問題研究会(SIG-FPAI), 12 Dec. 2016, 人工知能学会, Invited

  
• Machine Learning Applications:Past, Present and Future

  Hiroshi Mamitsuka

  Fifth International Symposium on Integrated Uncertainty in Knowledge Modeling and Decision Making (IUKM 2016), 30 Nov. 2016, Invited

  
• Collaborative Matrix Factorization for Predicting Drug-Target Interactions

  Hiroshi Mamitsuka

  Seminar, 29 Nov. 2016, University of Engineering and Technology, National University of Vietnam in Hanoi

  
• Label Propagation over Multiple Graphs

  Hiroshi Mamitsuka

  Seminar, 28 Nov. 2016, Hanoi University of Science and Technology

  
• Recent Development in Bioinformatics

  馬見塚 拓

  セミナー, 27 Jul. 2016, 協和発酵キリン株式会社 探索研究所, Invited

  
• Machine Learning for Predicting Cleavability of Calpain Proteolysis

  Hiroshi Mamitsuka

  'The Biology of Calpain in Health and Disease', FASEB Science Research Conference, 17 Jul. 2016, Invited

  
• Collaborative Matrix Factorization for Predicting Drug-Target Interactions

  Hiroshi Mamitsuka

  Seminar, 03 Dec. 2015, Humboldt-Universitat zu Berlin, Invited

  
• Greetings from Kyoto

  Hiroshi Mamitsuka

  Caroline von-Humboldt Professorship Award Ceremony, 01 Dec. 2015, Humboldt-Universitat zu Berlin, Invited

  
• Panel session

  Hiroshi Mamitsuka

  CIM Workshop on Machine Learning, 09 Oct. 2015, Uppsala University, Invited

  
• Collaborative Matrix Factorization for Predicting Drug-Target Interactions

  Hiroshi Mamitsuka

  CIM Workshop on Machine Learning, 08 Oct. 2015, Uppsala University, Invited

  
• Collaborative Matrix Factorization for Predicting Drug-Target Interactions

  Hiroshi Mamitsuka

  Seminar, 21 Jan. 2015, National Tsing Hua University, Invited

  
• Mining from Biological Networks: Preserving Network Localities

  Hiroshi Mamitsuka

  Seminar, 24 Oct. 2014, Fudan University, Invited

  
• Mining from Biological Networks: Preserving Network Localities

  Hiroshi Mamitsuka

  Seminar, 30 Sep. 2014, Lille 1 University, Invited

  
• Data-integrative Approach for Genomics

  Hiroshi Mamitsuka

  Sweden-Kyoto Symposium 2014, 12 Sep. 2014, Kyoto University, Invited

  
• Mining from Biological Networks: Preserving Network Localities

  Hiroshi Mamitsuka

  2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 22 May 2014, Computational Intelligence Society, IEEE, Invited

  
• Roundtable: Future Directions in Computational Intelligence in Bioinformatics Research

  Hiroshi Mamitsuka

  2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 21 May 2014, Computational Intelligence Society, IEEE, Invited

  
• Mining Metabolic Pathways through Gene Expression

  Hiroshi Mamitsuka

  euSYSBIO ? Master・Efs Degree Programme in Computational and Systems Biology, 28 Apr. 2014, Erasmus Mundus, Invited

  
• Mining Patterns from Trees

  Hiroshi Mamitsuka

  euSYSBIO ? Master・Efs Degree Programme in Computational and Systems Biology, 25 Apr. 2014, Erasmus Mundus, Invited

  
• Mining Patterns from Sequences: Hidden Markov Models and Learning

  Hiroshi Mamitsuka

  euSYSBIO ? Master・Efs Degree Programme in Computational and Systems Biology, 24 Apr. 2014, Erasmus Mundus, Invited

  
• Collaborative Matrix Factorization for Predicting Drug-Target Interactions

  Hiroshi Mamitsuka

  Seminar, Lille 1 University, 26 Mar. 2014, Centre Europ?en pour les Math?matiques, la Physique et leurs interactions (CEMPI), Invited

  
• Collaborative Matrix Factorization for Predicting Drug-Target Interactions

  Hiroshi Mamitsuka

  Trends in Machine Learning (TML) 2014, 18 Mar. 2014, Kyoto University, Invited

  
• Collaborative Matrix Factorization for Predicting Drug-Target Interactions

  Hiroshi Mamitsuka

  Seminar, 03 Feb. 2014, Aalto University, Invited

  
• Mining Metabolic Pathways through Gene Expression

  Hiroshi Mamitsuka

  Seminar, 20 Dec. 2013, National University of Singapore, Invited

  
• Label Propagation over Graphs

  Hiroshi Mamitsuka

  Seminar, 19 Dec. 2013, Institute for Infocomm Research (I2R), A*STAR, Singapore, Invited

  
• Efficiently Detecting Switching Mechanisms in Gene Expression

  Hiroshi Mamitsuka

  24th International Conference on Genome Informatics (GIW 2013), 17 Dec. 2013, Invited

  
• Introduction of Institute for Chemical Research, Kyoto University

  Hiroshi Mamitsuka

  The HUPO Initiative Assembly in Kyoto, 19 Sep. 2013, HUPO, Invited

  
• Mining Significant Substructure Pairs from Drug-Target Network

  馬見塚 拓

  日本バイオインフォマティクス学会 第17回創薬インフォマティクス研究会, 11 Sep. 2013, Invited

  
• Mining Metabolic Pathways through Gene Expression

  Hiroshi Mamitsuka

  Kyoto University - Koc University Symposium ``New Frontiers in Health Sciences & Technologies", 07 Sep. 2013, Kyoto University, Invited

  
• Opening Remark

  Hiroshi Mamitsuka

  2013 International Workshop on Machine Learning and Applications to Biology, 05 Aug. 2013, Hokkaido University

  
• Opening and Closing Remarks

  Hiroshi Mamitsuka

  13th Annual International Workshop on Bioinformatics and Systems Biology, 31 Jul. 2013

  
• Machine Learning for Bioinformatics

  Hiroshi Mamitsuka

  FASEB Science and Research Conference, ``The Biology of Calpains in Health and Disease'', 22 Jul. 2013, Federation of American Societies for Experimental Biology (FASEB), Invited

  
• Label Propagation over Graphs

  Hiroshi Mamitsuka

  Seminar, 08 Mar. 2013, Mitsubishi Electric Research Laboratories, Invited

  
• Mining Metabolic Pathways through Gene Expression

  馬見塚 拓

  日本バイオインフォマティクス学会 九州地域部会 セミナー, 08 Feb. 2013, 九州大学生体防御医学研究所セミナー, Invited

  
• Mining Metabolic Pathways through Gene Expression

  馬見塚 拓

  第1回JHUPOサテライトシンポジウム, 18 Jan. 2013, Invited

  
• Label Propagation over Graphs

  Hiroshi Mamitsuka

  Seminar, 17 Dec. 2012, Noah's Ark Lab, Huawei, Invited

  
• Mining Metabolic Pathways through Gene Expression

  Hiroshi Mamitsuka

  Kyoto University - Durham University Joint International Symposium, 28 Nov. 2012, Invited

  
• 遺伝子発現データ解析 —スイッチング機構の発見とパスウェイマイニング—

  馬見塚 拓

  第15回新潟プロテオミクスフォーラム, 20 Nov. 2012, 新潟, Invited

  
• Label Propagation over Graphs

  Hiroshi Mamitsuka

  Seminar, 08 Oct. 2012, IBM T. J. Watson Research Center, Invited

  
• Mining Subgraph-Subsequence Pairs from Drug-Target Network

  Hiroshi Mamitsuka

  2012 Workshop on Pharmaco-Informatics for Drug Discovery, in Conjunction with 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2012), 04 Oct. 2012, Computer Society, IEEE, Invited

  
• Opening Remark

  Hiroshi Mamitsuka

  2012 Sapporo Workshop on Machine Learning and Application to Biology (MLAB), 06 Aug. 2012, Hokkaido University, Invited

  
• Machine Learning for Systems Biology

  Hiroshi Mamitsuka

  Faculty Lab Presentation, International Workshop of Bioinformatics and Systems Biology 2012, 26 Jul. 2012, Boston University, Invited

  
• Efficiently Detecting Switching Mechanisms in Gene Expression

  馬見塚 拓

  セミナー, 02 Feb. 2012, 協和発酵キリン株式会社 探索研究所, Invited

  
• 薬剤−ターゲットネットワークからの部分構造パタン検出と解析

  馬見塚 拓

  高知大学大学院医学専攻DCセミナー(大学院公開セミナー), 30 Jan. 2012, 高知大学医学部, Invited

  
• Clustering over Graphs: Probabilistic Model based Approach

  Hiroshi Mamitsuka

  Seminar in Information Systems and Applications, 23 Nov. 2011, National Tsing Hua University, Invited

  
• Label Propagation over Graphs

  Hiroshi Mamitsuka

  Seminar, National Tsing Hua University, 22 Nov. 2011, National Tsing Hua University

  
• Efficient Label Propagation over Graphs

  Hiroshi Mamitsuka

  Seminar, 14 Nov. 2011, Fudan University

  
• Data Mining-based Approach for Drug-Target Prediction

  Hiroshi Mamitsuka

  Invited Tutorial, 2011 International Conference on Neural Information Processing (ICONIP), Tutorials, 13 Nov. 2011, Invited

  
• 生命科学上の非構造化データからのマイニング

  馬見塚 拓

  第13回新潟プロテオミクスフォーラム, 26 Oct. 2011, Invited

  
• Opening Remark

  Hiroshi Mamitsuka

  ICR Symposium to Celebrate the Bioinformatics Center's 10 Year Anniversary and New Restructuring, 29 Aug. 2011, Institute for Chemical Research (ICR), Kyoto University, Invited

  
• Mining from Graphs

  Hiroshi Mamitsuka

  FICS Summer School, 24 Aug. 2011, Finnish Doctoral Programme in Computational Sciences (FICS), Invited

  
• 代謝ネットワークからのマイニング

  馬見塚 拓

  教育セミナー、日本プロテオーム学会2011年大会, 28 Jul. 2011, Invited

  
• Probabilistic Models for Labelled Ordered Trees: Applications to Glycans

  Hiroshi Mamitsuka

  Summer School 2011, 22 Jul. 2011, Computational Systems Biology, DFG-GRADUIERTENKOLLEG 1772 - Research Training Group

  
• Mining Significant Substructure Pairs from Drug-Target Network

  馬見塚 拓

  セミナー, 21 Jun. 2011, 協和発酵キリン株式会社 探索研究所, Invited

  
• Mining Significant Substructure Pairs from Drug-Target Network

  Hiroshi Mamitsuka

  Seminar, 26 May 2011, University of Strasbourg

  
• PathRanker: Mining Metabolic Pathways through Gene Expression

  Hiroshi Mamitsuka

  Seminar, 24 May 2011, Institute of Genetics and Molecular and Cellular Biology (IGBMC), University of Strasbourg, Invited

  
• Mining Significant Substructure Pairs from Drug-Target Network

  Hiroshi Mamitsuka

  Seminar, 16 May 2011, Fudan University, Invited

  
• Mining Metabolic Pathways through Gene Expression

  Hiroshi Mamitsuka

  The 2nd Workshop on Bioinformatics for Medical and Pharmaceutical Research, 28 Feb. 2011, Curie Institute, Invited

  
• Mining Metabolic Pathways through Gene Expression

  Hiroshi Mamitsuka

  Seminar, 23 Feb. 2011, Humboldt University, Computational Systems Biology, DFG-GRADUIERTENKOLLEG 1772 - Research Training Group, Invited

  
• Clustering over Graphs: Probabilistic Model based Approach

  Hiroshi Mamitsuka

  Seminar, 20 Jan. 2011, National Cheng Kung University, Invited

  
• Clustering Genes by Using Expression and Gene Network Topology

  Hiroshi Mamitsuka

  Seminar, 19 Jan. 2011, National Cheng Kung University, Invited

  
• Mining Metabolic Pathways through Gene Expression

  Hiroshi Mamitsuka

  Seminar, 30 Dec. 2010, Fudan University, Invited

  
• Clustering over Graphs: Probabilistic Model Based Approach

  Hiroshi Mamitsuka

  Seminar, 28 Dec. 2010, Shanghai Jiao Tong University, Invited

  
• Mining Metabolic Pathways through Gene Expression

  Hiroshi Mamitsuka

  Seminar, 27 Dec. 2010, Shanghai Institutes for Biological Sciences, Invited

  
• Mining Metabolic Pathways through Gene Expression

  Hiroshi Mamitsuka

  Seminar, 10 Dec. 2010, Boston College, Invited

  
• Clustering Genes by Using Expression and Gene Network Topology

  Hiroshi Mamitsuka

  Seminar, 08 Dec. 2010, Boston University, Invited

  
• 生命科学上の非構造化データの統合マイニング

  馬見塚 拓

  バイオインフォマティクス推進センター事業、第６回研究開発成果報告会, 10 Nov. 2010, 品川

  
• Mining Metabolic Pathways through Gene Expression

  Hiroshi Mamitsuka

  2010 Systems Biology and Bioinformatics Symposium (SBBS), 05 Nov. 2010, Taiwan Society for Bioinformatics and Systems Biology (TBSB), Invited

  
• ライフサイエンスデータからのマイニング

  馬見塚 拓

  第17回京都大学化学研究所 公開講演会, 24 Oct. 2010

  
• Probabilistic Model Learning for Clustering with Multiple Graphs

  Hiroshi Mamitsuka

  Seminar, 18 Oct. 2010, Fudan University, Invited

  
• Probabilistic Model Learning for Clustering with Multiple Graphs

  Hiroshi Mamitsuka

  Seminar, 15 Oct. 2010, SooChow University, Invited

  
• Clustering over Graphs

  Hiroshi Mamitsuka

  Second International Seminar on Business and Information Management (ISBIM 2010), 13 Sep. 2010, Invited

  
• ネットワークからの遺伝子クラスタリング

  馬見塚 拓

  公開シンポジウム“統合複雑系科学への招待”, 05 Aug. 2010, Invited

  
• Ranking Metabolic Paths Based on Adjacent Pairwise Coexpression

  Hiroshi Mamitsuka

  Seminar, 13 May 2010, Fudan University, Invited

  
• Efficiently Detecting a Switching Mechanism in Gene Expression

  Hiroshi Mamitsuka

  Seminar, 22 Mar. 2010, Curie Institute, Invited

  
• Efficiently Finding a Switching Mechanism in Gene Expression

  Hiroshi Mamitsuka

  Seminar, 16 Mar. 2010, Fudan University, Invited

  
• Mining patterns from trees - Probabilistic model-based approach

  馬見塚 拓

  日本バイオインフォマティクス学会 第９回北海道地域部会セミナー, 08 Feb. 2010, 北海道大学, Invited

  
• Mining Significant Patterns from Carbohydrate Sugar Chains

  Hiroshi Mamitsuka

  Seminar, 09 Oct. 2009, Humboldt University, Invited

  
• Mining Significant Patterns from Glycan Structures

  Hiroshi Mamitsuka

  International Beilstein Symposium on Glyco-Bioinformatics, 05 Oct. 2009, Beilstein Institute, Invited

  
• A Markov Classification Model for Metabolic Pathways

  Hiroshi Mamitsuka

  Seminar, 29 Sep. 2009, Fudan University, Invited

  
• Mining Significant Patterns from Trees

  Hiroshi Mamitsuka

  TISE Summer School on Statistical Modeling and Machine Leraning in Computational Systems Biology, 24 Jun. 2009, Tampere Graduate School in Information Science and Engineering (TISE), Invited

  
• Probabilistic Models for Mining from Labeled Ordered Trees: Application to Glycobiology

  Hiroshi Mamitsuka

  TISE Summer School on Statistical Modeling and Machine Leraning in Computational Systems Biology, 24 Jun. 2009, Tampere Graduate School in Information Science and Engineering (TISE), Invited

  
• Learning Probabilistic Models for Time-series Data: Hidden Markov Models

  Hiroshi Mamitsuka

  TISE Summer School on Statistical Modeling and Machine Leraning in Computational Systems Biology, 22 Jun. 2009, Tampere Graduate School in Information Science and Engineering (TISE), Invited

  
• Clustering with Heterogeneous Data

  Hiroshi Mamitsuka

  IEEE International Conference on Computational Intelligence and Natural Computing (CINC 2009), 06 Jun. 2009, Invited

  
• Mining Significant Patterns from Trees

  Hiroshi Mamitsuka

  Seminar, 28 May 2009, Universit? Louis Pasteur, Invited

  
• A Probabilistic Model-based Approach for Mining Chemical Compound-Gene Co-occurrences from Biomedical Texts

  Hiroshi Mamitsuka

  Seminar, 25 May 2009, Universit? Louis Pasteur, Invited

  
• Mining Significant Patterns from Trees

  Hiroshi Mamitsuka

  Bioinformatics Workshop, 13 May 2009, Shanghai Key Laboratory of Intelligent Information Processing, Invited

  
• Mining Significant Patterns from Trees

  Hiroshi Mamitsuka

  Seminar, 24 Mar. 2009, I2R, A*STAR, Singapore, Invited

  
• 発現情報に基づく効率的な代謝経路ランキング手法を適用したリグニン生合成経路解析

  馬見塚 拓

  科研費ワークショップ「生薬とバイオインフォマティクス」, 15 Jan. 2009, Invited

  
• Mining Significant Patterns from Trees

  Hiroshi Mamitsuka

  IEEE International Symposium on Knowledge Acquisition and Modeling (KAM 2008), 21 Dec. 2008, Beijin Section, IEEE; HuaZhong Normal Uniersity, Invited

  
• Clustering Genes with Microarray Expressions and Gene Networks

  Hiroshi Mamitsuka

  Seminar, 22 Oct. 2008, Max Planck Institute for Biological Cybernetics, Invited

  
• 半構造化データからのマイニング

  馬見塚 拓

  葉山セミナー, 14 Oct. 2008, 総合研究大学院大学先導科学研究科, Invited

  
• Clustering Numerical Vectors with a Modularity Network

  Hiroshi Mamitsuka

  Workshop on "Relations with Computer Science", Foundations of Computational Mathematics (FoCM'08), 22 Jun. 2008, Invited

  
• ベクトル形式データとネットワークデータを統合したクラスタリング

  馬見塚 拓

  電子情報通信学会「パターン認識・メディア理解研究会」「データ工学研究会」共催、2008年6月研究会, 19 Jun. 2008, 電子情報通信学会, Invited

  
• Clustering Numerical Vectors with a Modularity Network

  馬見塚 拓

  セミナー, 17 Jun. 2008, 奈良先端科学技術大学院大学, Invited

  
• Clustering Numerical Vectors with a Modularity Network

  Hiroshi Mamitsuka

  Seminar, 27 May 2008, University of Melbourne, Invited

  
• Data-integrative Informatics for Chemical Genomics

  Hiroshi Mamitsuka

  Systems Biology Workshops - From Molecules to Life -, 27 May 2008, Invited

  
• Clustering Numerical Vectors with Modularity Networks

  Hiroshi Mamitsuka

  Seminar, 13 Dec. 2007, Helsinki University of Technology, Invited

  
• Opening Address

  Hiroshi Mamitsuka

  Eighteenth International Conference on Genome Informatics, 03 Dec. 2007, Invited

  
• An Integrative Approach for Gene Annotation based on Spectral Clustering and Network Modularity

  Hiroshi Mamitsuka

  AASBi Symposium, 02 Dec. 2007, The Association of Asian Societies for Bioinformatics (AASBi), Invited

  
• Clustering Numerical Vectors with Modularity Networks

  Hiroshi Mamitsuka

  Seminar, 28 Nov. 2007, Institute for Infocomm Research (I2R), A*STAR, Singapore, Invited

  
• Informatics Innovations in Glycogenomics

  馬見塚 拓

  21世紀COEプログラム「ゲノム科学の知的情報基盤・研究拠点形成」最終年度公 開シンポジウム：Symposium on Bioinformatics and Chemical Genomics, 20 Sep. 2007, 21世紀COEプログラム「ゲノム科学の知的情報基盤・研究拠点形成」, Invited

  
• Random Field, Network Modularity, Spectral Clustering and Beyond

  Hiroshi Mamitsuka

  International Conference on Intelligent Computing (ICIC 2007), 22 Aug. 2007, Invited

  
• Clustering Numerical Vectors with a Modular Network

  Hiroshi Mamitsuka

  Seminar, 10 Aug. 2007, University of California, Berkeley, Invited

  
• Closing Remark

  Hiroshi Mamitsuka

  International Symposium on Bioinformatics Education and Research, 17 Dec. 2006, Education and Research Organization for Genome Information Science, Invited

  
• A Probabilistic Model for Mining Labeled Ordered Trees and Its Application to Capturing Patterns in Carbohydrate Sugar Chains

  Hiroshi Mamitsuka

  International Workshop on Scientific Computing: Models, Algorithms and Applications, 08 Dec. 2006, Invited

  
• Learning a Probabilistic Model for Labeled Ordered Trees

  Hiroshi Mamitsuka

  Second Japan-Taiwan Bilateral Symposium on Bioinformatics, 09 Nov. 2006, Interchange Association, Invited

  
• Learning Probabilistic Models for Mining Labeled Ordered Trees

  Hiroshi Mamitsuka

  Third Japanese-German Frontiers of Sciences Symposium, 03 Nov. 2006, https://www.humboldt-foundation.de/web/5060.html, Invited

  
• バイオインフォマティクス先端技術の現状と展望

  馬見塚 拓

  セミナー, 26 Sep. 2006, 協和発酵工業 バイオフロンティア研究所, Invited

  
• A New Probabilistic Model for Mining Labeled Ordered Trees

  Hiroshi Mamitsuka

  Seminar, 24 Aug. 2006, IBM T. J. Watson Research Center, Invited

  
• 能動学習を用いた効率的なMHC結合ペプチド予測

  馬見塚 拓

  臨床研セミナー, 25 May 2006, 東京都臨床医学総合研究所, Invited

  
• Biological Sequence Analysis with a Probabilistic Model

  馬見塚 拓

  セミナー, 28 Mar. 2006, 千葉大学, Invited

  
• Probabilistic Model for Mining Labeled Ordered Trees: Capturing Patterns in Carbohydrate Sugar Chains

  Hiroshi Mamitsuka

  Seminar, 23 Mar. 2006, National University of Singapore, Invited

  
• Probabilistic Model for Mining Labeled Ordered Trees: Capturing Patterns in Carbohydrate Sugar Chains

  Hiroshi Mamitsuka

  Seminar, 21 Mar. 2006, Institute for Infocomm Research (I2R), A*STAR, Singapore, Invited

  
• A Probabilistic Model-based Approach for Biomedical Text Mining

  Hiroshi Mamitsuka

  First Japan-Taiwan Bilateral Symposium on Bioinformatics, 14 Mar. 2006, Interchange Association, Invited

  
• Opening Remark

  Hiroshi Mamitsuka

  Sixteenth International Conference on Genome Informatics, 19 Dec. 2005, Invited

  
• バイオインフォマティクス人材養成のあるべき姿とは？

  馬見塚 拓

  産業技術総合研究所 生命情報科学人材養成コース 最終シンポジウム「バイオインフォマティクス人材養成5年の歩み お台場から世界に向けて」, 22 Sep. 2005, 産業技術総合研究所 生命情報科学センター, Invited

  
• マルコフ連鎖の有限混合モデルによるマイクロアレイデータからの代謝パスウェイ解析

  馬見塚 拓

  統計関連学会連合大会企画セッション「アレイデータ解析周辺にみる新しい統計的視点」, 13 Sep. 2005, Invited

  
• バイオインフォマティクス先端技術の現状と展望

  馬見塚 拓

  セミナー, 27 Jun. 2005, シスメックス株式会社中央研究所・テクノセンター, Invited

  
• バイオインフォマティクス先端技術の現状と展望

  馬見塚 拓

  セミナー, 25 Jun. 2005, キリンビール株式会社 医薬探索研究所, Invited

  
• 糖鎖構造からのデータマイニング

  馬見塚 拓

  公開シンポジウム「ゲノムからケミカルゲノムへ」, 24 Mar. 2005, 京都大学21世紀COEプログラム「ゲノム科学の知的情報基盤・研究拠点形成」, Invited

  
• 糖鎖構造からのデータマイニング

  馬見塚 拓

  セミナー, 02 Mar. 2005, 日本電気（株）, Invited

  
• 代謝パスウェイの発現プロファイルによる解析

  馬見塚 拓

  第27回日本分子生物学会年会ワークショップ, 09 Dec. 2004, Invited

  
• バイオインフォマティクスへの機械学習応用

  馬見塚 拓

  バイオインフォマティクス夏の学校, 21 Jul. 2004, 日本バイオインフォマティクス学会, Invited

  
• プロテオームインフォマティクスの現状と展望

  馬見塚 拓

  日本SGIソリューションフェア2003, 05 Nov. 2003, 日本SGI, Invited

  
• ゲノム・プロテオーム情報のデータマイニング

  馬見塚 拓

  京都市酒蔵バイオＶＩＬ開所一周年記念事業 「21世紀COEシンポジウム」, 11 Aug. 2003, 京都大学21世紀COEプログラム「ゲノム科学の知的情報基盤・研究拠点形成」, Invited

  
• Hierarchical Latent Knowledge Analysis on Co-occurrence Data

  馬見塚 拓

  情報処理学会、電子情報通信学会北海道支部共催講演会, 24 Jun. 2003, 情報処理学会、電子情報通信学会, Invited

  
• 潜在変数モデルによるタンパク質相互作用の予測

  馬見塚 拓

  第３回日本蛋白質科学会年会ワークショップ（プロテインインフォマティクス）, 23 Jun. 2003, 日本蛋白質科学会, Invited

  
• 階層型潜在変数モデルによるタンパク質間相互作用の予測

  馬見塚 拓

  第１回「高次構造からみるプロテオーム機能発現メカニズム」研究会, 21 May 2003, Invited

  
• Predicting Protein-Protein Interactions with Latent Variable Models

  Hiroshi Mamitsuka

  Second Asian Joint Workshop on Protein Informatics, 27 Feb. 2003, Invited

  
• プロテオーム情報と低分子化合物情報の効率的な解析

  馬見塚 拓

  人工知能学会第23回分子生物情報研究会, 20 Jan. 2003, 人工知能学会, Invited

  
• Active Ensemble Learning

  馬見塚 拓

  セミナー(コロキウム), 10 Mar. 2002, 京都大学化学研究所附属バイオインフォマティクスセンター, Invited

  
• Active Ensemble Learning

  馬見塚 拓

  第4回情報論的学習理論ワークショップ(IBIS2001), 30 Jul. 2001, Invited

  
• HMM及び確率的文法を用いたタンパク質構造・機能予測について

  馬見塚 拓

  第165回CBI研究講演会, 15 Dec. 1997, Chem-Bio Informatics Society (CBI学会), Invited

  
• Predicting Beta-sheets Using Stochastic Tree Grammars

  馬見塚 拓

  セミナー(第2回ゲノム情報コロキウム), 22 Apr. 1996, 東京大学医科学研究所ヒトゲノム解析センター, Invited

  
• 確率的規則に基づくタンパク質αヘリックス領域予測

  馬見塚 拓

  第55回日本行動計量学会シンポジウム, 27 Feb. 1993, 日本行動計量学会, Invited

  
• Predicting Alpha-helix Using Stochastic Rule Learning

  馬見塚 拓

  遺伝子情報処理ワークショップ, 11 Nov. 1992, (財)新世代コンピュータ技術開発機構(ICOT), Invited

  

Books and Other Publications

• Machine Learning for Marketing

  Hiroshi Mamitsuka, Single work

  Global Data Science Publishing, Jun. 2019, Not refereed

  Find Kyoto University's collection of books :

  
• Textbook of Machine Learning and Data Mining (with Bioinformatics Applications)

  Hiroshi Mamitsuka, Single work

  Global Data Science Publishing, Sep. 2018, Not refereed

  Find Kyoto University's collection of books :

  
• Methods in Molecular Biology, Volume 1807: Data Mining for Systems Biology: Methods and Protocols, 2nd Edition

  Hiroshi Mamitsuka, Editor

  Humana Press (Springer), 2018, Not refereed

  Find Kyoto University's collection of books :

  
• Methods in Molecular Biology, Volume 993: In Silico Models for Drug Discovery

  Takigawa, I; Tsuda, K; Mamitsuka, H, Contributor, An In Silico Model for Interpreting Polypharmacology in Drug-Target Networks.

  Humana Press (Springer), Apr. 2013, Not refereed

  Find Kyoto University's collection of books :

  
• Methods in Molecular Biology, Volume 939: Data Mining for Systems Biology: Methods and Protocols

  Mamitsuka, H; DeLisi, C; Kanehisa, M, Joint editor

  Humana Press (Springer), Nov. 2012, Not refereed

  Find Kyoto University's collection of books :

  
• Identifying Pathways of Co-ordinated Gene Expression.

  Hancock, T; Takigawa, I; Mamitsuka, H, Contributor, Identifying Pathways of Co-ordinated Gene Expression.

  Humana Press (Springer), Nov. 2012, Not refereed

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• Biological Data Mining in Protein Interaction Networks.

  Wan, R; Mamitsuka, H, Contributor, Discovering Network Motifs in Protein Interaction Networks.

  IGI Global, May 2009, Not refereed

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• Genome Informatics, Volume 19

  Ng, S.-K; Mamitsuka, H; Wong, L, Joint editor

  Imperial College Press, 2007, Not refereed

  Find Kyoto University's collection of books :

  
• Genome Informatics, Volume 16, No. 2

  Heinrich, R; Mamitsuka, H; Kanehisa, M; Takagi, T; Miyano, S, Joint editor

  Universal Academy Press, 2005, Not refereed

  Find Kyoto University's collection of books :

  
• Genome Informatics, Volume 15, No.1

  Mamitsuka, H; Smith, T. F; Holzhutter, H.-G; Kanehisa, M; DeLisi, C; Heinrich, R; Miyano, S, Joint editor

  Universal Academy Press, 2004, Not refereed

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• バイオインフォマティクスがわかる

  宇高 恵子; 馬見塚 拓, Contributor, 主要組織適合性抗原結合性ペプチド予測

  羊土社, 2003, Not refereed

  Find Kyoto University's collection of books :

  
• Lecture Notes in Computer Science, Vol. 2281: Progress in Discovery Science

  Mamitsuka, H; Abe, N, Contributor, Efficient Data Mining by Active Learning

  Springer Berlin Heidelberg, Apr. 2002, Not refereed

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• Foundations of Real World Intelligence

  Abe, N; Yamanishi, K; Nakamura, A; Mamitsuka, H; Takeuchi, J; Li, H, Contributor, Distributed and Active Learning.

  Center for the Study of Language and Information (CSLI) Publications, Sep. 2001, Not refereed

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• 発見科学とデータマイニング

  安倍 直樹; 馬見塚 拓, Contributor, 能動学習と発見科学

  共立出版, Jun. 2001, Not refereed

  Find Kyoto University's collection of books :

  

Industrial Property Rights

• US 11533862, US16/624,584, Method and system for selecting a plant variety

  Jussi Gillberg; Samuel Kaski; Pekka Marttinen; Hiroshi Mamitsuka

  
• US 6973446, US20010003817, Knowledge finding method

  Hiroshi Mamitsuka; Naoki Abe

  
• 特許3237606, 特開平11-296553, 特願平10-116084, 複数文字列アライメント方法およびシステム

  馬見塚 拓

  
• 特許3094860, 特開平09-050289, 特願平07-219708, 隠れマルコフモデル学習システム及び学習方法

  馬見塚 拓

  
• 特許3012441, 特開平07-105179, 特願平05-246805, タンパク質立体構造予測方法

  馬見塚 拓

  
• 特許2980037, 特開平10-114792, 特願平08-268681, 構造分割装置

  馬見塚 拓

  
• 特許2940529, 特開平10-095796, 特願平9-283233, タンパク質立体構造予測方法

  馬見塚 拓

  
• 特許2870458, 特開平9-054780, 特願平7-227235, 学習装置及び学習方法

  馬見塚 拓; 中村 篤祥; 鳥羽 弘康

  
• 特許2739825, 特開平08-069446, 特願平06-203547, タンパク質立体構造予測方法

  馬見塚 拓

  
• 特許2658823, 特開平07-093286, 特願平05-233822, タンパク質立体構造予測方法

  馬見塚 拓; 安倍 直樹

  
• 特許2551297, 特開平07-013959, 特願平04-124817, タンパク質立体構造予測方法

  馬見塚 拓; 山西 健司

  

Awards

• Nov. 2014

  Broad-DREAM Gene Essentiality Prediction Challenge, Best Performer
• 2014

  IEEE Kansai Section, IEEE Kansai Section Medal
• 2009

  IEEE Computer Society, Certificate of Appreciation, Service Award

External funds: Kakenhi

• Latent data structure estimation through integrating diverse data

  Grant-in-Aid for Scientific Research (B)

  Basic Section 61030:Intelligent informatics-related

  Kyoto University

  馬見塚 拓

  From 01 Apr. 2022, To 31 Mar. 2025, Granted

  機械学習

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-22H03645/
• Development of new fabrication methods of polymer materials based on the structurally controlled hyperbranched polymers

  Grant-in-Aid for Scientific Research (S)

  Broad Section E

  Kyoto University

  山子 茂

  From 05 Jul. 2021, To 31 Mar. 2026, Granted

  超分岐ポリマー;デンドリマー;制御ラジカル重合;絡み合い;修復性材料;多分岐ポリマー;TERP;機能性高分子

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-21H05027/
• Developing machine learning based bioinformatics to decipher hidden biology of depression symptoms

  Grant-in-Aid for JSPS Fellows

  Basic Section 51030:Pathophysiologic neuroscience-related

  Kyoto University

  From 13 Nov. 2020, To 31 Mar. 2023, Granted

  バイオインフォマティクス

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-20F20809/
• Efficient estimation of data structure from multiple tensors

  Grant-in-Aid for Scientific Research (B)

  Basic Section 61030:Intelligent informatics-related

  Kyoto University

  馬見塚 拓

  From 01 Apr. 2019, To 31 Mar. 2022, Granted

  機械学習;高次元機械学習

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-19H04169/
• Estimating the factor structure in multiple matrices

  Grant-in-Aid for Scientific Research (B)

  Kyoto University

  Hiroshi Mamitsuka

  From 01 Apr. 2016, To 31 Mar. 2019, Project Closed

  機械学習;行列分解;協調行列分解;データマイニング;知識発見とデータマイニング

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-16H02868/
• Analysis of Repetition Structure in Huge Sequences

  Grant-in-Aid for Scientific Research (B)

  Hokkaido University

  Atsuyoshi Nakamura

  From 01 Apr. 2013, To 31 Mar. 2016, Project Closed

  知識発見とデータマイニング;シーケンスマイニング;ゲノム情報処理;頻出パターンマイニング

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-25280079/
• Estimating data structure embedded in semi-structured data

  Grant-in-Aid for Scientific Research (B)

  Kyoto University

  Hiroshi Mamitsuka

  From 01 Apr. 2012, To 31 Mar. 2016, Project Closed

  知識発見とデータマイニング;グラフマイニング;機械学習;生体生命情報学;人工知能;ゲノム;プロテオーム

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-24300054/
• 生体内化合物の効率的な比較、探索、発見アルゴリズムの開発

  Grant-in-Aid for Scientific Research on Priority Areas

  Biological Sciences

  Kyoto University

  馬見塚 拓

  From 01 Apr. 2004, To 31 Mar. 2005, Project Closed

  アルゴリズム;機械学習;生体生命情報学;プロテオーム

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-16014211/
• 生体高分子と結合する低分子化合物の効率的な比較、探索、発見アルゴリズムの開発

  Grant-in-Aid for Scientific Research on Priority Areas

  Biological Sciences

  Kyoto University

  馬見塚 拓

  From 01 Apr. 2003, To 31 Mar. 2004, Project Closed

  アルゴリズム;機械学習;生体生命情報学;プロテオーム

  https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-15014217/

External funds: others

• International Research and Training Program of Bioinformatics and Systems Biology

  独立行政法人日本学術振興会 若手研究者インターナショナル・トレーニング・プログラム (ITP)

  From 01 Apr. 2013, To 31 Mar. 2014

  馬見塚 拓
• International Research and Training Program of Bioinformatics and Systems Biology

  独立行政法人日本学術振興会 若手研究者インターナショナル・トレーニング・プログラム (ITP)

  From 01 Apr. 2012, To 31 Mar. 2013

  馬見塚 拓
• International Research and Training Program of Bioinformatics and Systems Biology

  独立行政法人日本学術振興会 若手研究者インターナショナル・トレーニング・プログラム (ITP)

  From 01 Apr. 2011, To 31 Mar. 2012

  馬見塚 拓
• International Research and Training Program of Bioinformatics and Systems Biology

  独立行政法人日本学術振興会 若手研究者インターナショナル・トレーニング・プログラム (ITP)

  From 01 Apr. 2010, To 31 Mar. 2011

  馬見塚 拓
• International Research and Training Program of Bioinformatics and Systems Biology

  独立行政法人日本学術振興会 若手研究者インターナショナル・トレーニング・プログラム (ITP)

  From 01 Apr. 2009, To 31 Mar. 2010

  馬見塚 拓

Education

Teaching subject(s)

• From 01 Apr. 2023, To 31 Mar. 2024

  Research Proposal Writing Practice B

  8403, Year-long, Graduate School of Pharmaceutical Sciences, 1
• From 01 Apr. 2023, To 31 Mar. 2024

  Research Proposal Writing Practice A

  8402, Year-long, Graduate School of Pharmaceutical Sciences, 1
• From 01 Apr. 2023, To 31 Mar. 2024

  Industry-academia Collaborative Internship A

  8305, Year-long, Graduate School of Pharmaceutical Sciences, 1
• From 01 Apr. 2023, To 31 Mar. 2024

  International Skills Practice A

  8303, Year-long, Graduate School of Pharmaceutical Sciences, 1
• From 01 Apr. 2023, To 31 Mar. 2024

  Laboratory Practice in Medicinal Frontier Sciences

  4213, Year-long, Graduate School of Pharmaceutical Sciences, 1
• From 01 Apr. 2023, To 31 Mar. 2024

  Pharmaceutical Laboratory Coaching Practice B

  8302, Year-long, Graduate School of Pharmaceutical Sciences, 1
• From 01 Apr. 2023, To 31 Mar. 2024

  Pharmaceutical Laboratory Coaching Practice A

  8301, Year-long, Graduate School of Pharmaceutical Sciences, 1
• From 01 Apr. 2023, To 31 Mar. 2024

  Advanced Bioinformatics for Drug Discovery

  8207, Fall, Graduate School of Pharmaceutical Sciences, 1
• From 01 Apr. 2023, To 31 Mar. 2024

  Research Training(Bio-Knowledge Engineering)

  D174, Year-long, Faculty of Pharmaceutical Sciences, 10
• From 01 Apr. 2023, To 31 Mar. 2024

  Research Training(Computational Genomics)

  D161, Year-long, Faculty of Pharmaceutical Sciences, 10
• From 01 Apr. 2023, To 31 Mar. 2024

  Medicinal Frontier Science Research A

  8406, Year-long, Graduate School of Pharmaceutical Sciences, 4
• From 01 Apr. 2023, To 31 Mar. 2024

  Skill Development for Chemoinformatics

  5002, Year-long, Graduate School of Pharmaceutical Sciences, 4
• From 01 Apr. 2023, To 31 Mar. 2024

  Research in Medicinal Frontier Sciences

  3215, Year-long, Graduate School of Pharmaceutical Sciences, 4
• From 01 Apr. 2023, To 31 Mar. 2024

  Medicinal Frontier Science Research B

  8407, Year-long, Graduate School of Pharmaceutical Sciences, 4
• From 01 Apr. 2023, To 31 Mar. 2024

  Research Training(Computational Genomics)

  8161, Year-long, Faculty of Pharmaceutical Sciences, 6
• From 01 Apr. 2023, To 31 Mar. 2024

  Seminar on Critical Reading of Scientific Papers A

  8404, Year-long, Graduate School of Pharmaceutical Sciences, 2
• From 01 Apr. 2023, To 31 Mar. 2024

  Introduction to Bioinformatics for Drug Discovery

  8104, Spring, Graduate School of Pharmaceutical Sciences, 2
• From 01 Apr. 2023, To 31 Mar. 2024

  Seminar in Medicinal Frontier Sciences

  2316, Year-long, Graduate School of Pharmaceutical Sciences, 2
• From 01 Apr. 2023, To 31 Mar. 2024

  Seminar on Critical Reading of Scientific Papers B

  8405, Year-long, Graduate School of Pharmaceutical Sciences, 2
• From 01 Apr. 2023, To 31 Mar. 2024

  Basic Laboratory Techniques in Physical Chemistry

  1206, Spring, Graduate School of Pharmaceutical Sciences, 2
• From 01 Apr. 2023, To 31 Mar. 2024

  Introduction to Bioinformatics for Drug Discovery

  1114, Spring, Graduate School of Pharmaceutical Sciences, 2
• From 01 Apr. 2022, To 31 Mar. 2023

  Introduction to Bioinformatics for Drug Discovery

  1114, Spring, Graduate School of Pharmaceutical Sciences, 2
• From 01 Apr. 2022, To 31 Mar. 2023

  Research Proposal Writing Practice A

  8402, Year-long, Graduate School of Pharmaceutical Sciences, 1
• From 01 Apr. 2022, To 31 Mar. 2023

  Research Training(Computational Genomics)

  8161, Year-long, Faculty of Pharmaceutical Sciences, 6
• From 01 Apr. 2022, To 31 Mar. 2023

  Medical Frontier Science Research A

  8406, Year-long, Graduate School of Pharmaceutical Sciences, 4
• From 01 Apr. 2022, To 31 Mar. 2023

  Research in Bioinformatics

  7004, Year-long, Graduate School of Pharmaceutical Sciences, 4
• From 01 Apr. 2022, To 31 Mar. 2023

  Seminar in Medicinal Frontier Sciences

  2316, Year-long, Graduate School of Pharmaceutical Sciences, 2
• From 01 Apr. 2022, To 31 Mar. 2023

  Seminar on Critical Reading of Scientific Papers A

  8404, Year-long, Graduate School of Pharmaceutical Sciences, 2
• From 01 Apr. 2022, To 31 Mar. 2023

  Advanced Methods in Bioinformatics

  5401, Year-long, Graduate School of Pharmaceutical Sciences, 2
• From 01 Apr. 2022, To 31 Mar. 2023

  Research in Bioinformatics

  5201, Year-long, Graduate School of Pharmaceutical Sciences, 4
• From 01 Apr. 2022, To 31 Mar. 2023

  Research in Medicinal Frontier Sciences

  3215, Year-long, Graduate School of Pharmaceutical Sciences, 4
• From 01 Apr. 2022, To 31 Mar. 2023

  Introduction to Bioinformatics for Drug Discovery

  8104, Spring, Graduate School of Pharmaceutical Sciences, 2
• From 01 Apr. 2022, To 31 Mar. 2023

  Research Training(Computational Genomics)

  D161, Year-long, Faculty of Pharmaceutical Sciences, 10
• From 01 Apr. 2022, To 31 Mar. 2023

  Pharmaceutical Laboratory Coaching Practice A

  8301, Year-long, Graduate School of Pharmaceutical Sciences, 1
• From 01 Apr. 2022, To 31 Mar. 2023

  Research in Bioinformatics

  7004, Year-long, Graduate School of Pharmaceutical Sciences, 4
• From 01 Apr. 2022, To 31 Mar. 2023

  Laboratory Practice in Medicinal Frontier Sciences

  4213, Year-long, Graduate School of Pharmaceutical Sciences, 1
• From 01 Apr. 2022, To 31 Mar. 2023

  Skill Development for Chemoinformatics

  5002, Year-long, Graduate School of Pharmaceutical Sciences, 4
• From 01 Apr. 2022, To 31 Mar. 2023

  Advanced Methods in Bioinformatics

  7006, Year-long, Graduate School of Pharmaceutical Sciences, 2
• From Apr. 2011, To Mar. 2012

  特別実習(分子設計情報)

  Year-long, 薬学部
• From Apr. 2011, To Mar. 2012

  Introduction to Bioinformatics

  Spring, 薬学研究科
• From Apr. 2011, To Mar. 2012

  Skill Development for Chemoinformatics

  Year-long, 薬学研究科
• From Apr. 2011, To Mar. 2012

  Skill Development for Chemoinformatics

  Year-long, 薬学研究科
• From Apr. 2011, To Mar. 2012

  Advanced Drug Discovery & Development II

  Spring, 薬学研究科
• From Apr. 2011, To Mar. 2012

  Advanced Methods in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2011, To Mar. 2012

  Advanced Methods in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2011, To Mar. 2012

  Research in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2011, To Mar. 2012

  Research in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2012, To Mar. 2013

  Advanced Drug Discovery & Development II

  Spring, 薬学研究科
• From Apr. 2012, To Mar. 2013

  doctoral program

  Spring, 薬学研究科
• From Apr. 2012, To Mar. 2013

  doctoral program

  Fall, 薬学研究科
• From Apr. 2012, To Mar. 2013

  Skill Development for Chemoinformatics

  Year-long, 薬学研究科
• From Apr. 2012, To Mar. 2013

  Introduction to Bioinformatics

  Spring, 薬学研究科
• From Apr. 2012, To Mar. 2013

  Research Training(Computational Genomics)

  Year-long, 薬学部
• From Apr. 2012, To Mar. 2013

  Advanced Methods in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2012, To Mar. 2013

  Research in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2013, To Mar. 2014

  Research Training(Computational Genomics)

  Year-long, 薬学部
• From Apr. 2013, To Mar. 2014

  Introduction to Bioinformatics

  Spring, 薬学研究科
• From Apr. 2013, To Mar. 2014

  Skill Development for Chemoinformatics

  Year-long, 薬学研究科
• From Apr. 2013, To Mar. 2014

  Research in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2013, To Mar. 2014

  Advanced Methods in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2013, To Mar. 2014

  Advanced Drug Discovery & Development II

  Spring, 薬学研究科
• From Apr. 2013, To Mar. 2014

  doctoral program

  Spring, 薬学研究科
• From Apr. 2013, To Mar. 2014

  doctoral program

  Fall, 薬学研究科
• From Apr. 2014, To Mar. 2015

  Research Training(Computational Genomics)

  Year-long, 薬学部
• From Apr. 2014, To Mar. 2015

  Introduction to Bioinformatics

  Spring, 薬学研究科
• From Apr. 2014, To Mar. 2015

  Skill Development for Chemoinformatics

  Year-long, 薬学研究科
• From Apr. 2014, To Mar. 2015

  Research in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2014, To Mar. 2015

  Advanced Methods in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2014, To Mar. 2015

  Research in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2014, To Mar. 2015

  Advanced Methods in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2015, To Mar. 2016

  Skill Development for Chemoinformatics

  Year-long, 薬学研究科
• From Apr. 2015, To Mar. 2016

  Introduction to Bioinformatics

  Spring, 薬学研究科
• From Apr. 2015, To Mar. 2016

  Research Training(Computational Genomics)

  Year-long, 薬学部
• From Apr. 2015, To Mar. 2016

  Advanced Methods in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2015, To Mar. 2016

  Advanced Methods in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2015, To Mar. 2016

  Research in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2015, To Mar. 2016

  Research in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2016, To Mar. 2017

  Skill Development for Chemoinformatics

  Year-long, 薬学研究科
• From Apr. 2016, To Mar. 2017

  Introduction to Bioinformatics

  Spring, 薬学研究科
• From Apr. 2016, To Mar. 2017

  Research Training(Computational Genomics)

  Year-long, 薬学部
• From Apr. 2016, To Mar. 2017

  Advanced Methods in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2016, To Mar. 2017

  Advanced Methods in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2016, To Mar. 2017

  Research in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2016, To Mar. 2017

  Research in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2017, To Mar. 2018

  Introduction to Bioinformatics for Drug Discovery

  Spring, 薬学研究科
• From Apr. 2017, To Mar. 2018

  Skill Development for Chemoinformatics

  Year-long, 薬学研究科
• From Apr. 2017, To Mar. 2018

  Research Training(Computational Genomics)

  Year-long, 薬学部
• From Apr. 2017, To Mar. 2018

  Advanced Methods in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2017, To Mar. 2018

  Research in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2018, To Mar. 2019

  Introduction to Bioinformatics for Drug Discovery

  Spring, 薬学研究科
• From Apr. 2018, To Mar. 2019

  Skill Development for Chemoinformatics

  Year-long, 薬学研究科
• From Apr. 2018, To Mar. 2019

  Research Training(Computational Genomics)

  Year-long, 薬学部
• From Apr. 2018, To Mar. 2019

  Advanced Methods in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2018, To Mar. 2019

  Research in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2018, To Mar. 2019

  Research in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2019, To Mar. 2020

  Introduction to Bioinformatics for Drug Discovery

  Spring, 薬学研究科
• From Apr. 2019, To Mar. 2020

  Skill Development for Chemoinformatics

  Year-long, 薬学研究科
• From Apr. 2019, To Mar. 2020

  Research Training(Computational Genomics)

  Year-long, 薬学部
• From Apr. 2019, To Mar. 2020

  Advanced Methods in Bioinformatics

  Spring, 薬学研究科
• From Apr. 2019, To Mar. 2020

  Advanced Methods in Bioinformatics

  Fall, 薬学研究科
• From Apr. 2019, To Mar. 2020

  Advanced Methods in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2019, To Mar. 2020

  Research in Bioinformatics

  Spring, 薬学研究科
• From Apr. 2019, To Mar. 2020

  Research in Bioinformatics

  Fall, 薬学研究科
• From Apr. 2019, To Mar. 2020

  Research in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2020, To Mar. 2021

  Introduction to Bioinformatics for Drug Discovery

  Spring, 薬学研究科
• From Apr. 2020, To Mar. 2021

  Skill Development for Chemoinformatics

  Year-long, 薬学研究科
• From Apr. 2020, To Mar. 2021

  Research Training(Computational Genomics)

  Year-long, 薬学部
• From Apr. 2020, To Mar. 2021

  Advanced Methods in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2020, To Mar. 2021

  Advanced Methods in Bioinformatics

  Spring, 薬学研究科
• From Apr. 2020, To Mar. 2021

  Advanced Methods in Bioinformatics

  Fall, 薬学研究科
• From Apr. 2020, To Mar. 2021

  Research in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2020, To Mar. 2021

  Research in Bioinformatics

  Spring, 薬学研究科
• From Apr. 2020, To Mar. 2021

  Research in Bioinformatics

  Fall, 薬学研究科
• From Apr. 2021, To Mar. 2022

  Introduction to Bioinformatics for Drug Discovery

  Spring, 薬学研究科
• From Apr. 2021, To Mar. 2022

  Skill Development for Chemoinformatics

  Year-long, 薬学研究科
• From Apr. 2021, To Mar. 2022

  Research Training(Computational Genomics)

  Year-long, 薬学部
• From Apr. 2021, To Mar. 2022

  Research Training(Computational Genomics)

  Year-long, 薬学部
• From Apr. 2021, To Mar. 2022

  Advanced Methods in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2021, To Mar. 2022

  Advanced Methods in Bioinformatics

  Spring, 薬学研究科
• From Apr. 2021, To Mar. 2022

  Advanced Methods in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2021, To Mar. 2022

  Research in Bioinformatics

  Year-long, 薬学研究科
• From Apr. 2021, To Mar. 2022

  Research in Bioinformatics

  Spring, 薬学研究科
• From Apr. 2021, To Mar. 2022

  Research in Bioinformatics

  Year-long, 薬学研究科

Participation in PhD Defense

• Establishing advanced deep learning models for predicting drug side effects

  NGUYEN DUC ANH, Graduate School of Pharmaceutical Sciences, Chief Examiner

  23 Mar. 2023
• シングルセルトランスクリプトーム解析を駆使したウイルス感染に対する動物種特異的自然免疫応答の同定

  ASO HIROFUMI, Graduate School of Pharmaceutical Sciences, Sub-chief Examiner

  23 Mar. 2023

Administration

School management (title, position)

• From 01 Apr. 2023, To 31 Mar. 2025

  バイオインフォマティクスセンター センター長
• From 01 Apr. 2011, To 31 Mar. 2013

  化学研究所附属バイオインフォマティクスセンター長
• From 01 Apr. 2019, To 31 Mar. 2021

  化学研究所附属バイオインフォマティクスセンター長

Faculty management (title, position)

• From 01 Apr. 2023, To 31 Mar. 2024

  化学研究所運営委員会
• From 01 Apr. 2023, To 31 Mar. 2024

  化学研究所情報セキュリティ委員会
• From 01 Apr. 2023, To 31 Mar. 2024

  化学研究所設備運営委員会
• From 01 Apr. 2023, To 31 Mar. 2024

  化学研究所ゲノムネット推進室運営委員会
• From 01 Apr. 2023, To 31 Mar. 2025

  環境・エネルギー専門委員会 委員
• From 01 May 2022, To 30 Apr. 2024

  宇治構内保安・交通対策委員会委員
• From 01 Apr. 2022, To 31 Mar. 2023

  化学研究所財務委員会 委員
• From 01 Apr. 2022, To 31 Mar. 2023

  化学研究所情報セキュリティ委員会委員
• From 01 Apr. 2022, To 31 Mar. 2024

  学術情報メディアセンター全国共同利用運営委員会委員
• From 01 Apr. 2022, To 31 Mar. 2024

  情報環境整備委員会情報ネットワーク専門委員会委員
• From 01 Oct. 2006, To 30 Sep. 2008

  計算機環境専門委員会 委員
• From 01 Oct. 2008, To 30 Sep. 2010

  計算機環境専門委員会 委員
• From 01 Apr. 2010, To 31 Mar. 2012

  学術情報メディアセンター全国共同利用運営委員会 委員
• From 01 Oct. 2010, To 30 Sep. 2012

  計算機環境専門委員会 委員
• From 01 Jul. 2011, To 30 Jun. 2013

  研究用計算機専門委員会 委員
• From 01 Apr. 2013, To 31 Mar. 2015

  化学研究所ゲノムネット推進室運営委員会 委員
• From 01 Apr. 2013, To 31 Mar. 2014

  化学研究所財務委員会 委員
• From 01 Apr. 2013, To 31 Mar. 2014

  化学研究所産学連携委員会 委員
• From 01 Apr. 2013, To 31 Mar. 2014

  化学研究所将来問題・研究活性化委員会 委員
• From 01 Apr. 2013, To 31 Mar. 2014

  化学研究所設備運営委員会 委員
• From 01 Apr. 2014, To 31 Mar. 2015

  化学研究所財務委員会 委員
• From 01 Apr. 2014, To 31 Mar. 2015

  化学研究所将来問題・研究活性化委員会 委員
• From 01 Apr. 2014, To 31 Mar. 2015

  化学研究所設備運営委員会 委員
• From 01 Apr. 2015, To 31 Mar. 2017

  化学研究所ゲノムネット推進室運営委員会 委員
• From 01 Apr. 2015, To 31 Mar. 2016

  化学研究所財務委員会 委員
• From 01 Apr. 2015, To 31 Mar. 2016

  化学研究所産学連携委員会 委員
• From 01 Apr. 2015, To 31 Mar. 2016

  化学研究所自己点検評価委員会 委員
• From 01 Apr. 2016, To 31 Mar. 2017

  化学研究所産学連携委員会 委員
• From 01 Apr. 2016, To 31 Mar. 2017

  化学研究所将来問題・研究活性化委員会 委員
• From 01 Apr. 2017, To 31 Mar. 2019

  化学研究所ゲノムネット推進室運営委員会 委員
• From 01 Apr. 2017, To 31 Mar. 2018

  化学研究所財務委員会 委員
• From 01 Apr. 2017, To 31 Mar. 2018

  化学研究所設備運営委員会 委員
• From 01 Apr. 2018, To 31 Mar. 2019

  化学研究所広報委員会 委員
• From 01 Apr. 2018, To 31 Mar. 2019

  化学研究所財務委員会 委員
• From 01 Apr. 2018, To 31 Mar. 2019

  化学研究所将来問題・研究活性化委員会 委員
• From 01 Apr. 2019, To 31 Mar. 2022

  化学研究所ゲノムネット推進室運営委員会 委員
• From 01 Apr. 2019, To 31 Mar. 2020

  化学研究所運営委員会 委員
• From 01 Apr. 2019, To 31 Mar. 2020

  化学研究所広報委員会 委員
• From 01 Apr. 2019, To 31 Mar. 2020

  化学研究所財務委員会 委員
• From 01 Apr. 2020, To 31 Mar. 2021

  化学研究所運営委員会委員
• From 01 Apr. 2020, To 31 Mar. 2021

  化学研究所自己点検評価委員会委員
• From 01 Apr. 2020, To 31 Mar. 2021

  化学研究所情報セキュリティ委員会委員
• From 01 Apr. 2020, To 31 Mar. 2022

  宇治事業所省エネルギー推進委員会
• From 01 Apr. 2021, To 31 Mar. 2023

  環境・エネルギー専門委員会　委員
• From 01 Apr. 2021, To 31 Mar. 2023

  情報環境機構KUINS利用負担金検討委員会　委員
• From 01 Apr. 2021, To 31 Mar. 2022

  化学研究所将来問題・研究活性化委員会委員
• From 01 Apr. 2021, To 31 Mar. 2022

  化学研究所広報委員会委員
• From 01 Apr. 2021, To 31 Mar. 2022

  化学研究所情報セキュリティ委員会委員
• From 01 Apr. 2021, To 31 Mar. 2023

  化学研究所ゲノムネット推進室運営委員会委員