京都大学化学研究所研究者情報   [ English | Japanese ]


馬見塚 拓 (まみつか ひろし)

バイオインフォマティクスセンター 生命知識工学
(薬学研究科医薬創成情報科学専攻 協力講座)
教授
博士(理学)
URL:http://www.bic.kyoto-u.ac.jp/pathway/mami/
E-mail: mami at kuicr.kyoto-u.ac.jp
Tel: +81-774-38-3023
Fax: +81-774-38-3037

研究・教育歴
    2005.4〜京都大学化学研究所バイオインフォマティクスセンター勤務
    2002.4〜2005.3京都大学化学研究所寄附研究部門勤務
    1991.4〜2002.3日本電気(株)勤務
    1999.10東京大学より博士(理学:情報科学)号取得
    1991.3東京大学大学院工学系研究科情報工学専攻修士課程修了
    1988.3東京大学理学部生物化学科卒業
専門分野
    データマイニング,機械学習,バイオインフォマティクス(生命情報科学),創薬バイオインフォマティクス
主な研究テーマ
  1. 統計的機械学習
  2. 統合型データマイニング/機械学習手法の研究・開発
  3. 半構造化データからのマイニング/機械学習手法の研究・開発
  4. グライコインフォマティクス
  5. ファーマコインフォマティクス
主な論文・著書
  1. Karasuyama, M. and Mamitsuka, H., Adaptive Edge Weighting for Graph-Based Learning Algorithms. Machine Learning, 106 (2), 307-335 (2017). [DOI]
  2. Yotsukura, S., duVerle D., Hancock, T., Natsume-Kitatani, Y. and Mamitsuka, H., Computational Recognition for Long Non-coding RNA (lncRNA): Software and Databases. Briefings in Bioinformatics, 18 (1), 9-27 (2017). [DOI]  [pubmed]
  3. Yotsukura, S., Karasuyama, M., Takigawa, I. and Mamitsuka, H., A Bioinformatics Approach for Understanding Genotype-phenotype Correlation in Breast Cancer. Big Data Analytics in Genomics Chapter 13, 397-428 (2016). [DOI]
  4. Gao, J., Yamada, M., Kaski, S., Mamitsuka, H. and Zhu, S., A Robust Convex Formulations for Ensemble Clustering. Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), 1476-1482 (2016). [PDF]
  5. Wicker, N., Nguyen, C. H. and Mamitsuka, H., Some Properties of a Dissimilarity Measure for Labeled Graphs. Publications Mathématiques de Besançon: Algèbre et Théorie des Nombres, Issue 2016, 81-90 (2016). [PDF]
  6. Peng, S., You, R., Wang, H., Zhai, C., Mamitsuka, H. and Zhu, S. , DeepMeSH: Deep Semantic Representation for Improving Large-scale MeSH Indexing. Bioinformatics 32 (12) (Proceedings of the 24th International Conference on Intelligent Systems for Molecular Biology (ISMB 2016)), i70-i79 (2016). [DOI]  [pubmed]
  7. Yuan, Q.-J., Gao, J., Wu, D., Zhang, S., Mamitsuka, H. and Zhu, S., DrugE-Rank: Improving Drug-Target Interaction Prediction of New Candidate Drugs or Targets by Ensemble Learning to Rank. Bioinformatics 32 (12) (Proceedings of the 24th International Conference on Intelligent Systems for Molecular Biology (ISMB 2016)), i18-i27 (2016). [DOI]  [pubmed]
  8. Natsume-Kitatani, Y. and Mamitsuka, H., Classification of Promoters based on the Combination of Core Promoter Elements Exhibits Different Histone Modification Patterns. PLoS One, 11 (3), e0151917 (2016). [DOI]  [pubmed]
  9. Mohamed, A., Nguyen, C. H. and Mamitsuka, H., NMRPro: An Integrated Web Component for Interactive Processing and Visualization of NMR Spectra. Bioinformatics, 32 (13), 2067-2068 (2016). [DOI]  [pubmed]
  10. Shinkai-Ouchi, F., Koyama, S., Ono, Y., Hata, S., Ojima, K., Shindo, M., duVerle, D., Kitamura, F., Doi, N., Takigawa, I., Mamitsuka, H. and Sorimachi, H., Predictions of Cleavability of Calpain Proteolysis by Quantitative Structure-Activity Relationship Analysis Using Newly Determined Cleavage Sites and Catalytic Efficiencies of an Oligopeptide Array. Molecular and Cellular Proteomics, 15, 1262-1280 (2016). [DOI]  [pubmed]
  11. Johnston, I., Hancock, T., Mamitsuka, H. and Carvalho, L., Gene-proximity Models for Genome-Wide Association Studies. Annals of Applied Statistics, 10 (3), 1217-1244 (2016). [DOI]
  12. Nguyen, C. H. and Mamitsuka, H., New Resistance Distances with Global Information on Large Graphs. Proceedings of the Nineteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2016) (JMLR Workshop and Conference Proceedings,51), 639-647 (2016). [PDF]
  13. Nakamura, A., Takigawa, I., Tosaka, H., Kudo, M. and Mamitsuka, H., Mining Approximate Patterns with Frequent Locally Optimal Occurrences. Discrete Applied Mathematics, 200, 123-152 (2016). [DOI]
  14. Mohamed, A., Nguyen, C. H. and Mamitsuka, H., Current Status and Prospects of Computational Resources for Natural Product Dereplication: A Review. Briefings in Bioinformatics, 17(2), 309-321 (2016). [DOI]  [pubmed]
  15. Xu, Y., Luo, C., Mamitsuka, H. and Zhu, S., MetaMHCpan, A Meta Apporach for Pan-specific MHC Peptide Binding Prediction. Vaccine Design: Methods and Protocols, Volume 2: Vaccines for Veterinary Diseases, Methods in Molecular Biology, 1404, 753-760 (2016). [DOI]  [pubmed]
  16. 馬見塚 拓, 機械学習による薬物分子-ターゲット相互作用予測 SAR News, 29, 2-8 (2015). [PDF]
  17. Zheng, X., Zhu, S., Gao, J. and Mamitsuka, H., Instance-wise Weighted Nonnegative Matrix Factorization for Aggregating Partitions with Locally Reliable Clusters. Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), 4091-4097 (2015). [PDF]
  18. Zhou, J., Shui, Y., Peng, S., Li, X., Mamitsuka, H. and Zhu, S., MeSHSim: An R/Bioconductor Package for Measuring Semantic Similarity over MeSH Headings and MEDLINE Documents Journal of Bioinformatics and Computational Biology, 13 (6), 1542002 (2015). [DOI]  [pubmed]
  19. Liu, K., Peng, S., Wu, J., Zhai, C., Mamitsuka H. and Zhu S., MeSHLabeler: Improving the Accuracy of Large-scale MeSH indexing by Integrating Diverse Evidence. Bioinformatics 31 (12) (Proceedings of the 23rd International Conference on Intelligent Systems for Molecular Biology (ISMB/ECCB 2015)), i339-i347 (2015). [DOI]  [pubmed]
  20. Baba, H, Takahara, J. and Mamitsuka, H., In Silico Predictions of Human Skin Permeability using Nonlinear Quantitative Structure-Property Relationship Models. Pharmaceutical Research, 32 (7), 2360-2371 (2015). [DOI]  [pubmed]
  21. Shiga, M. and Mamitsuka, H., Non-negative Matrix Factorization with Auxiliary Information on Overlapping Groups. IEEE Transactions on Knowledge and Data Engineering, 27 (6), 1615-1628 (2015). [DOI]
  22. Wang, B., Chen, X., Mamitsuka, H. and Zhu, S., BMExpert: Mining MEDLINE for Finding Experts in Biomedical Domains Based on Language Model. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12 (6), 1286-1294 (2015). [DOI]  [pubmed]
  23. Yotsukura, S. and Mamitsuka, H., Evaluation of Serum-based Cancer Biomarkers: A Brief Review from a Clinical and Computational Viewpoint. Critical Reviews in Oncology/Hematology, 93 (2), 103-115 (2015). [DOI]  [pubmed]
  24. Mohamed, A., Hancock, T., Nguyen, C. H. and Mamitsuka, H., NetPathMiner:R/Bioconductor Package for Network Path Mining through Gene Expression. Bioinformatics, 30 (21), 3139-3141 (2014). [DOI]  [pubmed]
  25. Kayano, M., Shiga, M. and Mamitsuka, H., Detecting Differentially Coexpressed Genes from Labeled Expression Data:A Brief Review. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11 (1), 154-167 (2014). [DOI]  [pubmed]
  26. Nguyen, C. H., Wicker, N. and Mamitsuka, H., Selecting Graph Cut Solutions via Global Graph Similarity. IEEE Transactions on Neural Networks and Learning Systems, 25 (7), 1407-1412 (2014). [DOI]
  27. Ding, H., Takigawa, I., Mamitsuka, H. and Zhu, S., Similarity-based Machine Learning Methods for Predicting Drug-target Interactions: A Brief Review. Briefings in Bioinformatics 15 (5), 737-747 (2014). [DOI]  [pubmed]
  28. Takahashi, K., Takigawa, I. and Mamitsuka, H., SiBIC: A Web Server for Generating Gene Set Networks Based on Biclusters Obtained by Maximal Frequent Itemset Mining. PLoS One, 8(12), e82890 (2013). [DOI]  [pubmed]
  29. Karasuyama, M. and Mamitsuka, H., Manifold-based Similarity Adaptation for Label Propagation. Proceedings of the Twenty-Seventh Annual Conference on Neural Information Processing Systems (NIPS 2013), 1547-1555 (2013). [Paper site]
  30. Karasuyama, M. and Mamitsuka, H., Multiple Graph Label Propagation by Sparse Integration. IEEE Transactions on Neural Networks and Learning Systems, 24 (12), 1999-2012 (2013). [DOI]  [pubmed]
  31. Zheng, X., Ding, H., Mamitsuka, H. and Zhu, S., Collaborative Matrix Factorization with Multiple Similarities for Predicting Drug-Target Interactions. Proceedings of the Nineteenth ACM SIGKDD International Conference On Knowledge Discovery and Data Mining (KDD 2013), 1025-1033 (2013). [DOI], [PDF (preprint)]
  32. Shiga, M. and Mamitsuka, H., Variational Bayes Co-clustering with Auxiliary Information. Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering (MultiClust 2013), Article No. 5 (2013). [DOI]
  33. Takigawa, I., Tsuda, K. and Mamitsuka, H., An In Silico Model for Interpreting Polypharmacology in Drug-Target Networks. In Silico Models for Drug Discovery, Methods in Molecular Biology, 993, Chapter 5, 67-80 (2013). [DOI]  [pubmed]
  34. Nakamura, A., Saito, T., Takigawa, I., Kudo, M. and Mamitsuka, H., Fast Algorithms for Finding a Minimum Repetition Representation of Strings and Trees. Discrete Applied Mathematics, 161 (10-11), 1556-1575 (2013). [DOI]
  35. Gu, J., Feng, W., Zeng, J., Mamitsuka, H. and Zhu, S., Efficient Semi-supervised MEDLINE Document Clustering with MeSH Semantic and Global Content Constraints. IEEE Transactions on Cybernetics, 43(4), 1265-1276 (2013). [DOI]
  36. Wicker, N., Nguyen, C. H. and Mamitsuka, H., A New Dissimilarity Measure for Comparing Labeled Graphs. Linear Algebra and its Applications, 438 (5), 2331-2338 (2013). [DOI]
  37. Yamamoto, T., Nakayama, K., Hirano, H., Tomonaga, T., Ishihama, Y., Yamada, T., Kondo, T., Kodera, Y., Sato, Y., Araki, N., Mamitsuka, H. and Goshima, N., Integrated View of the Human Chromosome X-centric Proteome Project. Journal of Proteome Research, 12 (1), 58-61 (2013). [DOI]  [pubmed]
  38. Takigawa, I. and Mamitsuka, H., Graph Mining: Procedure, Application to Drug Discovery and Recent Advance. Drug Discovery Today, 18 (1-2), 50-57 (2013). (Invited Review Paper) [DOI]  [pubmed]
  39. Hancock, T., Takigawa, I. and Mamitsuka, H., Identifying Pathways of Co-ordinated Gene Expression. Data Mining for Systems Biology: Methods and Protocols, Methods in Molecular Biology, 939, Chapter 7, 69-85 (2013). [DOI]  [pubmed]
  40. Mamitsuka, H., DeLisi, C., and Kanehisa, M., Data Mining for Systems Biology: Methods and Protocols. Methods in Molecular Biology, 939 (2013). (Edited book) [DOI]
  41. Nguyen, C. H. and Mamitsuka, H., Latent Feature Kernels for Link Prediction on Sparse Graphs. IEEE Transactions on Neural Networks and Learning Systems, 23 (11), 1793-1804 (2012). [DOI]  [pubmed]
  42. Hancock. T. and Mamitsuka, H., Boosted Network Classifiers for Local Feature Selection. IEEE Transactions on Neural Networks and Learning Systems, 23 (11), 1767-1778 (2012). [DOI]  [pubmed]
  43. Sorimachi, H., Mamitsuka, H. and Ono, Y., Understanding the Substrate Specificity of Conventional Calpains. Biological Chemistry, 393 (9), 853-871 (2012). (Invited Review Paper) [DOI]  [pubmed]
  44. Mamitsuka, H., Mining from Protein-Protein Interactions. WIREs Data Mining and Knowledge Discovery 2 (5), 400-410 (2012). (Invited Review Paper) [DOI]
  45. 茅野 光範、馬見塚 拓, ROS-DETによる遺伝子「スイッチ発現」検出 実験医学 30 (6), 969-974 (2012).
  46. Hancock, T., Wicker, N., Takigawa, I. and Mamitsuka, H., Identifying Neighborhoods of Coordinated Gene Expression and Metabolite Profiles. PLoS One 7 (2), e31345 (2012). [DOI]  [pubmed]
  47. Zhang, L., Chen, Y., Wong, H.-S., Zhou, S., Mamitsuka, H. and Zhu, S., TEPITOPEpan: Extending TEPITOPE for Peptide Binding Prediction Covering over 700 HLA-DR Molecules. PLoS One 7 (2), e30483 (2012). [DOI]  [pubmed]
  48. Zhang, L., Udaka, K, Mamitsuka, H. and Zhu, S., Toward More Accurate Pan-Specific MHC-Peptide Binding Prediction: A Review of Current Methods and Tools. Briefings in Bioinformatics 13 (3), 350-364 (2012). [DOI]  [pubmed]
  49. duVerle, D. and Mamitsuka, H., A Review of Statistical Methods for Prediction of Proteolytic Cleavage. Briefings in Bioinformatics 13 (3), 337-349 (2011). [DOI]  [pubmed]
  50. Shiga, M. and Mamitsuka, H., Efficient Semi-Supervised Learning on Locally Informative Multiple Graphs. Pattern Recognition 45 (3), 1035-1049 (2012). [DOI]
  51. Shiga, M. and Mamitsuka, H., A Variational Bayesian Framework for Clustering with Multiple Graphs. IEEE Transactions on Knowledge and Data Engineering 24 (4), 577-590 (2012). [DOI]
  52. 瀧川 一学、馬見塚 拓, 化学とグラフアルゴリズム 化学と教育 59 (9), 450-453 (2011).
  53. Natsume-Kitatani, Y., Shiga, M. and Mamitsuka, H., Genome-wide Integration on Transcription Factors, Histone Acetylation and Gene Expression Reveals Genes Co-regulated by Histone Modification Patterns. PLoS One 6 (7), e22281 (2011). [DOI]  [pubmed]
  54. Nguyen, C. H. and Mamitsuka, H., Discriminative Graph Embedding for Label Propagation. IEEE Transactions on Neural Networks 22 (9), 1395-1405 (2011). [DOI]  [pubmed]
  55. Nguyen, C. H. and Mamitsuka, H., Kernels for Link Prediction with Latent Feature Models. Lecture Notes in Computer Science (Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery of Databases (ECML PKDD 2011), Part II), 517-532 (2011). [DOI]
  56. Shiga, M. and Mamitsuka, H., Clustering Genes with Expression and Beyond. WIREs Data Mining and Knowledge Discovery 1 (6), 496-511 (2011). (Invited Review Paper) [DOI]
  57. duVerle, D., Ono, Y., Sorimachi, H. and Mamitsuka, H., Calpain Cleavage Prediction Using Multiple Kernel Learning. PLoS One 6 (5), e19035 (2011). [DOI]  [pubmed]
  58. Kayano, M., Takigawa, I., Shiga, M., Tsuda, K. and Mamitsuka, H., ROS-DET: Robust Detector of Switching Mechanisms in Gene Expression. Nucleic Acids Research 39 (11), e74 (2011). [DOI]  [pubmed]
  59. Takigawa, I., Tsuda, K. and Mamitsuka, H., Mining Significant Substructure Pairs for Interpreting Polypharmacology in Drug-target Network. PLoS One 6 (2), e16999 (2011). [DOI]  [pubmed]
  60. Mamitsuka, H., Glycoinformatics: Data Mining-based Approaches. Chimia 65 (1/2), 10-13 (2011). (Invited Review Paper) [DOI]  [pubmed]
  61. Shiga, M., Takigawa, I. and Mamitsuka, H., A Spectral Approach to Clustering Numerical Vectors as Nodes in a Network. Pattern Recognition 44 (2), 236-251 (2011). [DOI]
  62. Takigawa, I. and Mamitsuka, H., Efficiently Mining d-Tolerance Closed Frequent Subgraphs. Machine Learning 82 (2), 95-121 (2011). [DOI]
  63. Hu, X., Mamitsuka, H. and Zhu, S., Ensemble Approaches for Improving HLA Class I-peptide Binding Prediction. Journal of Immunological Methods 374 (1/2), 47-52 (2011). [DOI]  [pubmed]
  64. Hancock, T., Takigawa, I. and Mamitsuka, H., Mining Metabolic Pathways through Gene Expression. Bioinformatics 26 (17), 2128-2135 (2010). [DOI]  [pubmed]
  65. Hu, X., Zhou, W., Udaka, K., Mamitsuka, H. and Zhu, S., MetaMHC: A Meta Approach to Predict Peptides Binding to MHC Molecules. Nucleic Acids Research 38, W474-W479 (2010). [DOI]  [pubmed]
  66. Hancock, T. and Mamitsuka, H., Boosted Optimization for Network Classification. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010) (JMLR Workshop and Conference Proceedings, 9), 305-312 (2010). [PDF]
  67. Hancock, T. and Mamitsuka, H., A Markov Classification Model for Metabolic Pathways. Algorithms for Molecular Biology 5, 10 (2010). [DOI]  [pubmed]
  68. Nakamura, A., Saito, T., Takigawa, I., Mamitsuka, H. and Kudo, M., Algorithms for Finding a Minimum Repetition Representation of a String. Lecture Notes in Computer Science 6393 (Proceedings of the Seventeenth Symposium on String Processing and Information Retrieval (SPIRE 2010)), 185-190 (2010). [DOI]
  69. Shiga, M. and Mamitsuka, H., Variational Bayes Learning over Multiple Graphs. Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010) , 166-171 (2010). [DOI]
  70. Takigawa, I., Hashimoto, K., Shiga, M., Kanehisa, M. and Mamitsuka, H., Mining Patterns from Glycan Structures. Proceedings of the International Beilstein Symposium on Glyco-Bioinformatics, 13-24 (2010). [Proceedings]
  71. Li, L., Ching, W-K., Chan, Y-M. and Mamitsuka, H., On Network-based Kernel Methods for Protein-Protein Interactions with Applications in Protein Functions Prediction. Journal of Systems Science and Complexity 23 (5), 917-930 (2010). [DOI]
  72. Kayano, M., Takigawa, I., Shiga, M., Tsuda, K. and Mamitsuka, H., On the Performance of Methods for Finding a Switching Mechanism in Gene Expression. Genome Informatics 24 (Proceedings of the Tenth Annual Workshop on Bioinformatics and Systems Biology), 69-83 (2010). [DOI]  [pubmed]
  73. Kayano, M., Takigawa, I., Shiga, M., Tsuda, K. and Mamitsuka, H., Efficiently Finding Genome-wide Three-way Gene Interactions from Transcript- and Genotype-Data. Bioinformatics 25 (21), 2735-2743 (2009). [DOI]  [pubmed]
  74. Zhu, S., Zeng, J. and Mamitsuka, H., Enhancing MEDLINE Document Clustering by Incorporating MeSH Semantic Similarity. Bioinformatics 25 (15), 1944-1951 (2009). [DOI]  [pubmed]
  75. Zhu, S., Takigawa, I., Zeng, J. and Mamitsuka, H., Field Independent Probabilistic Model for Clustering Multi-Field Documents. Information Processing and Management 45 (5), 555-570 (2009). [DOI], [PDF]
  76. Hancock, T. and Mamitsuka, H., A Markov Classification Model for Metabolic Pathways. Lecture Notes in Bioinformatics 5724 (Proceedings of the Ninth Workshop on Algorithms in Bioinformatics (WABI 2009)), 121-132 (2009). [DOI]
  77. Wan, R., Kiseleva, L., Harada, H., Mamitsuka, H. and Horton, P., HAMSTER: Visualizing Microarray Experiments as a Set of Minimum Spanning Trees. Source Code for Biology and Medicine 4 8 (2009). [DOI]  [pubmed]
  78. Ching, W-K., Li, L.., Chan, Y-M. and Mamtisuka, H., A Study of Network-based Kernel Methods on Protein-Protein Interaction for Protein Functions Prediction. Lecture Notes in Operations Research (Proceedings of the Third International Symposium on Optimization and Systems Biology (OSB 2009)) 11, 25-32 (2009). [PDF]
  79. Wan, R, Vo, A. N. and Mamitsuka, H., Efficient Probabilistic Latent Semantic Analysis Through Parallelization. Lecture Notes in Computer Science 5839 (Proceedings of the Fifth Asian Information Retrieval Symposium (AIRS 2009)), 432-443 (2009). [DOI]
  80. Hancock, T. and Mamitsuka, H., Active Pathway Identification and Classification with Probabilistic Ensembles. Genome Informatics 22 (Proceedings of the Ninth Annual Workshop on Bioinformatics and Systems Biology), 30-40 (2009) [DOI]  [pubmed]
  81. duVerle, D., Takigawa, I., Ono, Y., Sorimachi, H. and Mamitsuka, H., CaMPDB: a Resource for Calpain and Modulatory Proteolysis. Genome Informatics 22 (Proceedings of the Ninth Annual Workshop on Bioinformatics and Systems Biology), 202-214 (2009). [DOI]  [pubmed]
  82. Li, L., Shiga, M., Ching, W.-K. and Mamitsuka, H., Annotating Gene Functions with Integrative Spectral Clustering on Microarray Expressions and Sequences. Genome Informatics 22 (Proceedings of the Ninth Annual Workshop on Bioinformatics and Systems Biology), 95-120 (2009). [DOI]  [pubmed]
  83. Wan, R. and Mamitsuka, H., Discovering Network Motifs in Protein Interaction Networks. Biological Data Mining in Protein Interaction Networks Chapter 8, 117-143 (2009). [DOI]
  84. Hashimoto, K., Takigawa, I., Shiga, M., Kanehisa, M. and Mamitsuka, H., Mining Significant Tree Patterns in Carbohydrate Sugar Chains. Bioinformatics 24 (16) (Proceedings of the Seventh European Conference on Computational Biology (ECCB 2008)), i167-i173 (2008). [DOI]  [pubmed]
  85. Hashimoto, K., Aoki-Kinoshita, K. F., Ueda, N., Kanehisa, M. and Mamitsuka, H., A New Efficient Probabilistic Model for Mining Labeled Ordered Trees Applied to Glycobiology. ACM Transactions on Knowledge Discovery From Data 2 (1), Article No.6 (2008). [DOI]
  86. Takigawa, I. and Mamitsuka, H., Probabilistic Path Ranking Based on Adjacent Pairwise Coexpression for Metabolic Transcripts Analysis. Bioinformatics 24 (2), 250-257 (2008). [DOI]  [pubmed]
  87. Mamitsuka, H., Informatic Innovations in Glycobiology: Relevance to Drug Discovery. Drug Discovery Today 13 (3/4), 118-123 (2008). (Invited Review Paper) [DOI]  [pubmed]
  88. Hancock, T. and Mamitsuka, H., Semi-Supervised Graph Partitioning with Decision Trees. Genome Informatics 20 (Proceedings of the Eighth Annual Workshop on Bioinformatics and Systems Biology), 102-111 (2008). [DOI]  [pubmed]
  89. Wan, R, Wheelock, Å. and Mamitsuka, H., A Framework for Determining Outlying Microarray Experiments. Genome Informatics 20 (Proceedings of the Eighth Annual Workshop on Bioinformatics and Systems Biology), 64-76 (2008). [DOI]  [pubmed]
  90. 志賀 元紀、瀧川 一学、馬見塚 拓, 多様なゲノムデータの統合的クラスタリング解析 生物物理 48(3), 190-194 (2008). [DOI]
  91. 馬見塚 拓、米屋 隆, PURE: PubMed文献検索支援システム 実験医学増刊 26(7):「生命研究への応用と開発が進むバイオデータベースとソフトウェア最前線」, 201-206 (2008).
  92. Shiga, M., Takigawa, I. and Mamitsuka, H., A Spectral Clustering Approach to Optimally Combining Numerical Vectors with a Modular Network. Proceedings of the Thirteenth ACM SIGKDD International Conference On Knowledge Discovery and Data Mining (KDD 2007), 647-656 (2007). [DOI], [PDF]
  93. Shiga, M., Takigawa, I. and Mamitsuka, H., Annotating Gene Function by Combining Expression Data with a Modular Gene Network. Bioinformatics 23 (13) (Proceedings of the Fifteenth International Conference on Intelligent Systems for Molecular Biology (ISMB 2007)), i468-i478 (2007). [DOI]  [pubmed]
  94. Yoneya, T. and Mamitsuka, H., A Hidden Markov Model-based Approach for Identifying Timing Differences in Gene Expression under Different Experiment. Bioinformatics 23 (7), 842-849 (2007). [DOI]  [pubmed]
  95. Zhu, S., Takigawa, I., Zhang, S. and Mamitsuka, H., A Probabilistic Model for Clustering Text Documents with Multiple Fields. Lecture Notes in Computer Science 4425 (Proceedings of the Twenty-Ninth European Conference on Information Retrieval (ECIR 2007)), 331-342 (2007). [DOI].
  96. Kadowaki, T., Wheelock, C. E., Adachi, T., Kudo, T., Okamoto, S., Tanaka, N., Tonomura, K., Tsujimoto, G., Mamitsuka, H., Goto, S. and Kanehisa, M., Identification of Endocrine Disruptor Biodegradation by Integration of Structure-activity Relationship with Pathway Analysis. Environmental Science & Technology, 41 (23), 7997-8003 (2007). [DOI]  [pubmed]
  97. Yoneya, T. and Mamitsuka, H., PURE: A PubMed Article Recommendation System Based on Content-based Filtering. Genome Informatics 18 (Proceedings of the Seventh Annual Workshop on Bioinformatics and Systems Biology), 267-276 (2007). [DOI]  [pubmed]
  98. Mamitsuka, H., Selecting Features in Microarray Classification Using ROC Curves. Pattern Recognition 39 (12), 2393-2404 (2006). [DOI]
  99. Wan, R., Takigawa, I. and Mamitsuka, H., Applying Gaussian Distribution-dependent Criteria to Decision Trees for High-Dimensional Microarray Data. Lecture Notes in Bioinformatics 4316 (Proceedings of 2006 VLDB Workshop on Data Mining in Bioinformatics), 40-49 (2006). [DOI]
  100. Hashimoto, K., Aoki-Kinoshita, K. F., Ueda, N., Kanehisa, M. and Mamitsuka, H., A New Efficient Probabilistic Model for Mining Labeled Ordered Trees. Proceedings of the Twelfth ACM SIGKDD International Conference On Knowledge Discovery and Data Mining (KDD 2006), 177-186 (2006). [DOI], [PDF]
  101. Aoki-Kinoshita, K. F., Ueda, N., Mamitsuka, H. and Kanehisa, M., ProfilePSTMM: Capturing Tree-structure Motifs in Carbohydrate Sugar Chains. Bioinformatics 22 (14) (Proceedings of the Fourteenth International Conference on Intelligent Systems for Molecular Biology (ISMB 2006)), e25-e34 (2006). [DOI]  [pubmed]
  102. Zhu, S., Okuno, Y., Tsujimoto, G. and Mamitsuka, H., Application of a New Probabilistic Model for Mining Implicit Associated Cancer Genes from OMIM and Medline. Cancer Informatics 2, 361-371 (2006). [Cancer Informatics]  [pubmed]
  103. Zhu, S., Udaka, K., Sidney, J., Sette, A., Aoki-Kinoshita, K. F. and Mamitsuka, H., Improving MHC Binding Peptide Prediction by Incorporating Binding Data of Auxiliary MHC Molecules. Bioinformatics 22 (13), 1648-1655 (2006). [DOI]  [pubmed]
  104. Mamitsuka, H., Query-Learning-Based Iterative Feature-Subset Selection for Learning from High-Dimensional Data Sets. Knowledge and Information Systems 9 (1), 91-108 (2006). [DOI]
  105. Cios, J. K., Mamitsuka, H., Nagashima, T. and Tadeusiewicz, R., Guest Editorial: Computational Intelligence in Soving Bioinformatics Problems. Artificial Intelligence in Medicine 35 (1), 1-8 (2005). [DOI]  [pubmed]
  106. Mamitsuka, H., Finding the Biologically Optimum Alignment of Multiple Sequences. Artificial Intelligence in Medicine 35 (1), 9-18 (2005). [DOI]  [pubmed]
  107. Zhu, S., Okuno, Y., Tsujimoto, G. and Mamitsuka, H., A Probabilistic Model for Mining Implicit "Chemical Compound - Gene" Relations from Literature. Bioinformatics 21 Supplement 2 (Proceedings of the Fourth European Conference on Computational Biology (ECCB/JBI2005)), ii245-ii251 (2005). [DOI]  [pubmed]
  108. Ueda, N., Aoki-Kinoshita, K. F., Yamaguchi, A., Akutsu, T. and Mamitsuka, H., A Probabilistic Model for Mining Labeled Ordered Trees: Capturing Patterns in Carbohydrate Sugar Chains. IEEE Transactions on Knowledge and Data Engineering 17(8), 1051-1064 (2005). [DOI]
  109. Mamitsuka, H., Mining New Protein-Protein Interactions - Using a Hierarchical Latent-variable Model to Determine the Function of a Functionally Unknown Protein. IEEE Engineering in Medicine and Biology Magazine 24 (3), 103-108 (2005). [DOI]  [pubmed]
  110. Mamitsuka, H., Essential Latent Knowledge for Protein-Protein Interactions: Analysis by Unsupervised Learning Approach. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2(2), 119-130 (2005). [DOI]  [pubmed]
  111. Mamitsuka, H., Efficient Unsupervised Mining from Noisy Co-occurrence Data. New Mathematics and Natural Computation 1(1), 173-193 (2005). [DOI]
  112. Aoki, K. F., Mamitsuka, H., Akutsu, T. and Kanehisa, M., A Score Matrix to Reveal the Hidden Links in Glycans. Bioinformatics 21(8), 1457-1463 (2005). [DOI]  [pubmed]
  113. Yamada, T., Kawashima, S., Mamitsuka, H., Goto, S. and Kanehisa, M., Comprehensive Analysis and Prediction of Synthetic Lethality Using Subcellular Locations. Genome Informatics 16(1), 150-158 (2005). [DOI]  [pubmed]
  114. Wan, R., Mamitsuka, H. and Aoki, K. F., Cleaning Microarray Expression Data Using Markov Random Field Based-on Profile Similarity. Proceedings of the Twentieth ACM Symposium on Applied Computing, 206-207 (2005). [DOI]
  115. Igarashi, Y., Aoki, K. F., Mamitsuka, H., Kuma, K. and Kanehisa, M., The Evolutionary Repertoires of the Eukaryotic-type ABC Transporters in terms of the Phylogeny of ATP-binding Domains in Eukaryotes and Prokaryotes. Molecular Biology and Evolution 21 (11), 2149-2160 (2004). [DOI]  [pubmed]
  116. Yamaguchi, A., Aoki, K. F. and Mamitsuka, H., Finding the Maximum Common Subgraph of a Partial k-Tree and a Graph with a Polynomially Bounded Number of Spanning Trees. Information Processing Letters 92 (2), 57-63 (2004). [DOI]
  117. Mamitsuka, H. and Okuno, Y., A Hierarchical Mixture of Markov Models for Finding Biologically Active Metabolic Paths using Gene Expression and Protein Classes. Proceedings of the IEEE Computational Systems Bioinformatics Conference (CSB 2004), 341-352 (2004). [DOI]  [pubmed]
  118. Aoki, K. F., Ueda, N., Yamaguchi, A., Kanehisa, M., Akutsu, T. and Mamitsuka, H., Application of a New Probabilistic Model for Recognizing Complex Patterns in Glycans. Bioinformatics 20 Supplement 1 (Proceedings of the Twelfth International Conference on Intelligent Systems for Molecular Biology (ISMB/ECCB 2004)), i6-i14 (2004). [DOI]  [pubmed]
  119. Aoki, K. F., Ueda, N., Yamaguchi, A., Akutsu, T., Kanehisa, M. and Mamitsuka, H., Managing and Analyzing Carbohydrate Data. ACM SIGMOD Record 33 (2), 33-38 (2004). [DOI]
  120. Aoki, K. F., Yamaguchi, A., Ueda, H., Akutsu, T., Mamitsuka, H., Goto, S. and Kanehisa, M., KCaM (KEGG Carbohydrate Matcher): A Software Tool for Analyzing the Structures of Carbohydrate Sugar Chains. Nucleic Acids Research 32, W267-W272 (2004). [DOI]  [pubmed]
  121. Ueda, N., Aoki, K. F. and Mamitsuka, H., A General Probabilistic Framework for Mining Labeled Ordered Trees. Proceedings of the Fourth SIAM International Conference on Data Mining (SDM 2004) , 357-368 (2004). [DOI]
  122. Mamitsuka, H., Okuno, Y. and Yamaguchi, A., Mining Biologically Active Patterns in Metabolic Pathways using Microarray Expression Profiles. ACM SIGKDD Explorations 5 (2), 113-121 (2003). [DOI]
  123. Yamaguchi, A. and Mamitsuka, H., Finding the Maximum Common Subgraph of a Partial k-Tree and a Graph with a Polynomially Bounded Number of Spanning Trees. Lecture Notes in Computer Science 2906 (Proceedings of the Fourteenth International Symposium on Algorithm and Computation (ISAAC 2003)), 58-67 (2003). [DOI]
  124. Aoki, K. F., Yamaguchi, A., Okuno, Y., Akutsu, T., Ueda, N., Kanehisa, M. and Mamitsuka, H., Efficient Tree Matching Methods for Accurate Carbohydrate Database Queries. Genome Informatics 14(Proceedings of Fourteenth International Conference on Genome Informatics), 134-143 (2003). [DOI]  [pubmed]
  125. 宇高 恵子、馬見塚 拓, 主要組織適合性抗原結合性ペプチド予測 バイオインフォマティクスがわかる , 68-71 (2003).
  126. Mamitsuka, H., Efficient Mining from Heterogeneous Data Sets for Predicting Protein-Protein Interactions. Proceedings of the Fourteenth International Workhop on Database and Expert Systems Applications, 32-36 (2003). [DOI]
  127. Mamitsuka, H., Selective Sampling with a Hierarchical Latent Variable Model. Lecture Notes in Computer Science 2810 (Proceedings of the Fifth International Symposium on Intelligent Data Analysis (IDA 2003)), 352-363 (2003). [DOI]
  128. Mamitsuka, H., Hierarchical Latent Knowledge Analysis for Co-occurrence Data. Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003), 504-511 (2003). [AAAI site]
  129. Mamitsuka, H., Efficient Unsupervised Mining from Noisy Data Sets: Application to Clustering Co-occurrence Data. Proceedings of the Third SIAM International Conference on Data Mining (SDM 2003), 239-243 (2003). [DOI]
  130. Mamitsuka, H., Detecting Experimental Noise in Protein-Protein Interactions with Iterative Sampling and Model-based Clustering. Proceedings of the Third IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2003) , 385-392 (2003). [DOI]
  131. Mamitsuka, H., Empirical Evaluation of Ensemble Feature Subset Selection Methods for Learning from a High-Dimensional Database in Drug Design. Proceedings of the Third IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2003) , 253-257 (2003). [DOI]
  132. Udaka, K., Mamitsuka, H., Nakaseko, H. and Abe, N., Empirical Evaluation of a Dynamic Experiment Design Method for Prediction of MHC Class-I-Binding Peptides. Journal of Immunology 169 (10), 5744-5753 (2002). [DOI]  [pubmed]
  133. Mamitsuka, H., Iteratively Selecting Feature Subsets for Mining from High-Dimensional Databases. Lecture Notes in Computer Science 2431 (Proceedings of the Sixth European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2002)), 361-372 (2002). [DOI]
  134. 馬見塚 拓、安倍 直樹, 集団質問学習 - データマイニング・バイオインフォマティクスへの展開 - (招待論文) 電子情報通信学会論文誌 J85-DII (5), 717-724 (2002). [IEICE]
  135. Udaka, K., Mamitsuka, H., Nakaseko, H. and Abe, N., Prediction of MHC Class I Binding Peptides by a Query Learning Algorithm Based on Hidden Markov Models. Journal of Biological Physics 28 (2), 183-194 (2002). [DOI]
  136. Mamitsuka, H. and Abe, N., Efficient Data Mining by Active Learning. Lecture Notes in Computer Science 2281 (Progress in Discovery Science), 258-267 (2002). [DOI]
  137. Abe, N., Yamanishi, K., Nakamura, A., Mamitsuka, H., Takeuchi, J. and Li, H., Distributed and Active Learning. Foundations of Real World Intelligence, 189-250 (2001).
  138. Mamitsuka, H. and Abe, N., Efficient Mining from Large Databases by Query Learning. Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000) , 575-582 (2000). [PDF]
  139. 安倍 直樹、馬見塚 拓, 能動学習と発見科学 発見科学とデータマイニング , 64-72 (2000).
  140. Mamitsuka, H., Predicting Peptides That Bind to MHC Molecules Using Supervised Learning of Hidden Markov Models. PROTEINS: Structure, Function, and Genetics 33 (4), 460-474 (1998). [DOI]  [pubmed]
  141. Abe, N. and Mamitsuka, H., Query Learning Strategies Using Boosting and Bagging. Proceedings of the Fifteenth International Conference on Machine Learning (ICML 98) , 1-9 (1998). [PDF]
  142. Abe, N., Mamitsuka, H. and Nakamura, A., Empirical Coomparison of Competing Query Learning Methods. Lecture Notes in Computer Science 1532 (Proceedings of the First International Conference on Discovery Science (DS 98)), 387-388 (1998). [DOI]
  143. Abe, N. and Mamitsuka, H., Predicting Protein Secondary Structures Using Stochastic Tree Grammars. Machine Learning 29 (2-3), 275-301 (1997). [DOI]
  144. Mamitsuka, H., Supervised Learning of Hidden Markov Models for Sequence Discrimination. Proceeds of the First International Conference on Computational Molecular Biology (RECOMB 97) , 202-208 (1997). [DOI]
  145. Mamitsuka, H., A Learning Method of Hidden Markov Models for Sequence Discrimination. Journal of Computational Biology 3 (3), 361-373 (1996). [DOI]  [pubmed]
  146. Mamitsuka, H., Representing Inter-Residue Dependencies in Protein Sequences with Probabilistic Networks. Computer Applications in the Biosciences 11 (4), 413-422 (1995). [DOI]  [pubmed]
  147. Mamitsuka, H. and Yamanishi, K., Alpha-Helix Region Prediction with Stochastic-rule Learning. Computer Applications in the Biosciences 11 (4), 399-411 (1995). [DOI]  [pubmed]
  148. Mamitsuka, H. and Abe, N., Predicting Location and Structure of Beta-Sheet Regions Using Stochastic Tree Grammars. Proceedings of the Second International Conferenceon Intelligent Systems for Molecular Biology (ISMB 94) , 276-284 (1994). [AAAI site]  [pubmed]
  149. Abe, N. and Mamitsuka, H., A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars. Proceedings of the Eleventh International Conference on Machine Learning (ML 94) , 3-11 (1994). [DOI]
  150. Mamitsuka, H. and Yamanishi, K., Protein Alpha-Helix Region Prediction Based on Stochastic Rule Learning. Proceedings of the Twenty-sixth Annual Hawaii International Conference on System Sciences (HICSS 26) 1, 659-668 (1993). [DOI]
  151. Mamitsuka, H. and Yamanishi, K., Protein Secondary Structure Prediction Based on Stochastic Rule Learning. Lecture Notes in Computer Science 743 (Proceedings of the Third Annual Workshop on Algorithmic Learning Theory (ALT 92)), 240-251 (1992). [DOI]


Update: Jan 24,2017