Faculty Information, Institute for Chemical Research, Kyoto University   [ English | Japanese ]


MAMITSUKA, Hiroshi

Bioinformatics Center - Bio-knowledge Engineering -
（Laboratory in the Graduate School of Pharmaceutical Sciences）
Professor
Dr.Sc., University of Tokyo
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
Academic career
    Apr. 2011-Mar. 2013, Apr. 2019-Mar. 2021, Apr. 2023-Present Director, Bioinformatics Center, Institute for Chemical Research, Kyoto University
    Apr. 2006-Present Professor, Graduate School of Pharmaceutical Sciences, Kyoto University
    Apr. 2005-Present Professor, Bioinformatics Center, Institute for Chemical Research, Kyoto University
    Apr. 2002-Mar. 2005 Visiting associate professor, Institute for Chemical Research, Kyoto University
    Apr. 1991-Mar. 2002Research staff member, NEC Corporation
    Oct. 1999PhD in Information Sciences, University of Tokyo
    Mar. 1991M.E. in Information Engineering, University of Tokyo
    Mar. 1988B.S. in Biochemistry and Biophysics, University of Tokyo
Research field
    Machine Learning, Data Mining, Bioinformatics, Computational Biology, Chemoinformatics and Systems Biology
Current research
  1. Hiroshi Mamtisuka has been working on mining/learning with semi-structured data, such as graphs, networks, trees and sequences, under various problem settings including clustering, classification, semi-supervised learning and mining frequent patterns. He is now focusing more on machine learning-based systems biology.
Selected publications
  1. Yan, Y., Wang, S., Liu, H., Mamitsuka, H. and Zhu, S., GORetriever: Reranking Protein-description-based GO Candidates by Literature-driven Deep Information Retrieval for Protein Function Annotation. Bioinformatics, 40 (Supplement 2) (Proceedings of the 23rd European Conference on Computational Biology (ECCB2024)), ii53-ii61 (2024). [DOI]  [pubmed]
  2. Cao, T., Sun, L., Nguyen, C. H. and Mamitsuka, H., Learning Low-Rank Tensor Cores with Probabilistic ℓ0-Regularized Rank Selection for Model Compression. Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024), 3780-3788 (2024). [DOI]
  3. Yoshikawa, C., Nguyen, D. A., Nakaji-Hirabayashi, T., Takigawa, I. and Mamitsuka, H., Graph Network-based Simulation of Multicellular Dynamics Driven by Concentrated Polymer Brush-modified Cellulose Nanofibers. ACS Biomaterials Science & Engineering, 10 (4), 2165-2176 (2024). [DOI]  [pubmed]
  4. Takahashi, K., Mamitsuka, H., Tosaka, M., Zhu, N. and Yamago, S., CoPolDB: A Copolymerization Database for Radical Polymerization. Polymer Chemistry, 15, 965-971 (2024). [DOI]
  5. Nguyen, D. A., Nguyen, C. H. and Mamitsuka, H., Central-Smoothing Hypergraph Neural Networks for Predicting Drug-Drug Interactions. IEEE Transactions on Neural Networks and Learning Systems, 35 (8), 11620-11625 (2024). [DOI]  [pubmed]
  6. Qu, W., You, R., Mamitsuka, H. and Zhu, S., DeepMHCI: An Anchor Position-aware Deep Interaction Model for Accurate MHC-I Peptide Binding Affinity Prediction. Bioinformatics, 39 (9), btad551 (2023). [DOI]  [pubmed]
  7. Wang, X., Sun, L., Nguyen, C. H. and Mamitsuka. H., Multiplicative Sparse Tensor Factorization for Multi-View Multi-Task Learning. Proceedings of the 26th European Conference on Artificial Intelligence (ECAI 2023) , 2560-2567, IOS Press (2023). [DOI]
  8. Liao, Z., Xie, L., Mamitsuka, H. and Zhu, S., Sc2Mol: A Scaffold-based Two-step Molecule Generator with Variational Autoencoder and Transformer. Bioinformatics, 39 (1), btac814 (2023). [DOI]  [pubmed]
  9. Nguyen, D. A., Nguyen, C. H., Petschner, P. and Mamitsuka, H., SPARSE: A Sparse Hypergraph Neural Network for Learning Multiple Types of Latent Combinations to Accurately Predict Drug-drug Interactions. Bioinformatics, 38 (Supplement 1) (Proceedings of the 30th International Conference on Intelligent Systems for Molecular Biology (ISMB 2022)), i333-i341 (2022). [DOI]  [pubmed]
  10. You, R., Qu, W., Mamitsuka, H. and Zhu, S., DeepMHCII: A Novel Binding Core-Aware Deep Interaction Model for Accurate MHC II-peptide Binding Affinity Prediction. Bioinformatics, 38 (Supplement 1) (Proceedings of the 30th International Conference on Intelligent Systems for Molecular Biology (ISMB 2022)), i220-i228 (2022). [DOI]  [pubmed]
  11. Güvenç, B. P., Kaski, S. and Mamitsuka, H., DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19 (4), 2197-2207 (2022). [DOI]  [pubmed]
  12. Liu, L, Mamitsuka, H. and Zhu, S., HPODNets: Deep Graph Convolutional Networks for Predicting Human Protein-phenotype Associations. Bioinformatics, 38 (3), 799-808 (2022). [DOI]  [pubmed]
  13. Hiremath, S., Wittke, S., Palosuo, T., Kaivosoja, J., Tao, F., Proll, M., Puttonen, E., Peltonen-Sainio, P., Marttinen, P. and Mamitsuka, H., Crop Loss Identification at Field Parcel Scale using Satellite Remote Sensing and Machine Learning. PLoS One, 16 (12), e0251952 (2021). [DOI]  [pubmed]
  14. Nguyen, D. H., Nguyen, C. H. and Mamitsuka, H., Machine Learning for Metabolic Identification. Creative Complex Systems, Chapter 20, 329-350, Springer (2021). [DOI]
  15. Liao, Z., Huang, X., Mamitsuka, H. and Zhu, S., Drug3D-DTI: Improved Drug-target Interaction Prediction by Incorporating Spatial Information of Small Molecules. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2021), 340-347 (2021). [DOI]
  16. Güvenç, B. P., Kaski, S. and Mamitsuka, H., Machine Learning Approaches for Drug Combination Therapies. Briefings in Bioinformatics, 22 (6), bbab293 (2021). [DOI]  [pubmed]
  17. Nguyen, D. H., Nguyen, C. H. and Mamitsuka, H., Learning Subtree Pattern Importance for Weisfeiler-Lehman based Graph Kernels. Machine Learning, 110 (7) 1585-1607 (2021). [DOI]
  18. You, R., Yao, S., Mamitsuka H. and Zhu, S., DeepGraphGO: Graph Neural Net for Large-scale, Multispecies Protein Function Prediction. Bioinformatics, 37 (Supplement 1) (Proceedings of the 29th International Conference on Intelligent Systems for Molecular Biology (ISMB/ECCB 2021)), i262-i271 (2021). [DOI]  [pubmed]
  19. Liu, L, Mamitsuka, H. and Zhu, S., HPOFiller: Identifying Missing Protein-phenotype Associations by Graph Convolutional Network. Bioinformatics, 37 (19), 3328-3336 (2021). [DOI]  [pubmed]
  20. Cai, M., Nguyen, C. H., Mamitsuka, H. and Li, L., XGSEA: CROSS-species Gene Set Enrichment Analysis via Domain Adaptation. Briefings in Bioinformatics, 22 (5), bbaa406 (2021). [DOI]  [pubmed]
  21. Güvenç, B. P., Mamitsuka, H. and Kaski, S., Improving Drug Response Prediction by Integrating Multiple Data Sources: Matrix Factorization, Kernel and Network-based Approaches. Briefings in Bioinformatics, 22 (1), 346-359 (2021). [DOI]  [pubmed]
  22. Nguyen, D. A., Nguyen, C. H. and Mamitsuka, H., A Survey on Adverse Drug Reaction Studies: Data, Tasks, and Machine Learning Methods. Briefings in Bioinformatics, 22 (1), 164-177 (2021). [DOI]  [pubmed]
  23. Kaneko, H., Blanc-Mathieu, R., Endo, H., Chaffron, S., Delmont, T. O., Gaia, M., Henry, N., Hernández-Velázquez, R., Nguyen, C.-H., Mamitsuka, H., Forterre, P., Jaillon, O., de Vargas, C., Sullivan, M. B., Suttle, C. A., Guidi, L. and Ogata, H. , Eukaryotic Virus Composition Can Predict the Efficiency of Carbon Export in the Global Ocean. iScience, 24 (1), 102002 (2021). [DOI]  [pubmed]
  24. Wimalawarne, K. and Mamitsuka, H., Reshaped Tensor Nuclear Norms for Higher Order Tensor Completion. Machine Learning, 110 (3), 507-531 (2021). [DOI]
  25. Yui, R., Liu, Y., Mamitsuka, H. and Zhu, S., BERTMeSH: Deep Contextual Representation Learning for Large-scale High-performance MeSH Indexing with Full Text. Bioinformatics, 37 (5), 684-692 (2021). [DOI]  [pubmed]
  26. Nguyen, C. H. and Mamitsuka, H., Learning on Hypergraphs with Sparsity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43 (8), 2710-2722 (2021). [DOI]  [pubmed]
  27. Liu, L., Huang, X., Mamitsuka, H. and Zhu, S., HPOLabeler: Improving Prediction of Human Protein-phenotype Associations by Learning to Rank. Bioinformatics, 36 (14), 4180-4188 (2020). [DOI]  [pubmed]
  28. Strahl, J., Peltonen, J., Mamitsuka, H. and Kaski, S., Scalable Probabilistic Matrix Factorization with Graph-Based Priors. Proceedings of the 34th AAAI Conference on Artificial Intelligence, 34, 5851-5858 (2020). [DOI]
  29. Nakamura, A., Takigawa, I. and Mamitsuka, H., Efficiently Enumerating Substrings with Statistically Significant Frequencies of Locally Optimal Occurrences in Gigantic String. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 5240-5247 (2020). [DOI]
  30. Wimalawarne, K., Yamada, M. and Mamitsuka, H., Scaled Coupled Norms and Coupled Higher Order Tensor Completion. Neural Computation, 32 (2), 447-484 (2020). [DOI]  [pubmed]
  31. Dai, S., You, R., Lu, Z., Huang, X., Mamitsuka, H. and Zhu, S., FullMeSH: Improving Large-Scale MeSH Indexing with Full Text. Bioinformatics, 36 (5), 1533-1541 (2020). [DOI]  [pubmed]
  32. Mamitsuka, H., Machine Learning for Marketing. Global Data Science Publishing. (2019). [Supporting Web Page]
  33. You, R., Dai, S., Zhang, Z., Mamitsuka, H. and Zhu, S., AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification. Proceedings of the 33rd Annual Conference on Neural Information Processing Systems (NeurIPS 2019), 5820-5830 (2019). [Paper site]
  34. Sun, L., Nguyen, C. H. and Mamitsuka, H., Multiplicative Sparse Feature Decomposition for Efficient Multi-View Multi-Task Learning. Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), 3506-3512 (2019). [DOI]
  35. Sun, L., Nguyen, C. H. and Mamitsuka, H., Fast and Robust Multi-View Multi-Task Learning via Group Sparsity. Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), 3499-3505 (2019). [DOI]
  36. You, R., Yao, S., Xiong, Y., Huang, X., Sun, F., Mamitsuka, H. and Zhu, S., NetGO: Improving Large-scale Protein Function Prediction with Massive Network Information. Nucleic Acids Research, 47 (W1), W379-W387 (2019). [DOI]  [pubmed]
  37. Nguyen, D. H., Nguyen, C. H. and Mamitsuka, H., ADAPTIVE: leArning DAta-dePendenT, concIse molecular VEctors for fast, accurate metabolite identification from tandem mass spectra. Bioinformatics, 35 (14) (Proceedings of the 27th International Conference on Intelligent Systems for Molecular Biology (ISMB/ECCB 2019)), i164-i172 (2019). [DOI]  [pubmed]
  38. Gillberg, J., Marttinen, P., Mamitsuka, H. and Kaski, S., Modelling G×E with Historical Weather Information Improves Genomic Prediction in New Environments. Bioinformatics, 35 (20), 4045-4052 (2019). [DOI]  [pubmed]
  39. Nguyen, D. H., Nguyen, C. H. and Mamitsuka, H., Recent Advances and Prospects of Computational Methods for Metabolite Identification: A Review with Emphasis on Machine Learning Approaches. Briefings in Bioinformatics, 20 (6) , 2028-2043 (2019). [DOI]  [pubmed]
  40. Eid, A., Mamitsuka, H. and Wicker, N., A Metropolis-Hastings Sampling of Subtrees in Graphs. Austrian Journal of Statistics, 48 (5), 17-33 (2019). [DOI]
  41. Wicker, N., Nguyen, C. H. and Mamitsuka, H., A p-Laplacian Random Walk: Application to Video Games. Austrian Journal of Statistics, 48 (5), 11-16 (2019). [DOI]
  42. duVerle, D. and Mamitsuka, H., CalCleaveMKL: a Tool for Calpain Cleavage Prediction. Calpain: Methods and Protocols, Methods in Molecular Biology, 1915, Chapter 11, 121-147 (2019). [DOI]  [pubmed]
  43. Gao, J., Liu, L., Yao, S., Huang, S., Mamitsuka, H. and Zhu, S., HPOAnnotator: Improving Large-scale Prediction of HPO Annotations by Low-rank Approximation with HPO Semantic Similarities and Multiple PPI Networks. BMC Medical Genomics, 12, 187 (2019). [DOI]  [pubmed]
  44. Mamitsuka, H., Textbook of Machine Learning and Data Mining (with Bioinformatics Applications). Global Data Science Publishing. (2018). [Supporting Web Page]
  45. Wimalawarme, K. and Mamitsuka, H., Efficient Convex Completion of Coupled Tensors using Coupled Nuclear Norms. Proceedings of the Thirty-Second Annual Conference on Neural Information Processing Systems (NeurIPS 2018), 6902-6910 (2018). [Paper site]
  46. Gao, J., Shuwei, Y., Mamitsuka, H. and Zhu, S., AiProAnnotator: Low-rank Approximation with Network Side Information for High-performance, Large-scale Human Protein Abnormality Annotator. Proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018), 13-20 (2018). [DOI]
  47. Wimalawarme, K., Yamada, M. and Mamitsuka, H., Convex Coupled Matrix and Tensor Completion. Neural Computation, 30 (12), 3095-3127 (2018). [DOI]  [pubmed]
  48. Nguyen, D. H., Nguyen, C. H. and Mamitsuka, H., SIMPLE: Sparse Interaction Model over Peaks of MoLEcules for Fast, Interpretable Metabolite Identification from Tandem Mass Spectra. Bioinformatics, 34 (12) (Proceedings of the 26th International Conference on Intelligent Systems for Molecular Biology (ISMB 2018)), i323-i332 (2018). [DOI]  [pubmed]
  49. You, R., Zhang, Z., Xiong, Y., Sun, F., Mamitsuka, H. and Zhu, S., GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank. Bioinformatics, 34 (14), 2465-2478 (2018). [DOI]  [pubmed]
  50. Yamada, M., Tang, J., Lugo-Martinez, J., Hodzic, E., Shrestha, R., Saha, A., Ouyang, H., Yin, D., Mamitsuka, H., Sahinalp, C., Radivojac, P., Menczer, F. and Chang, Y., Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data. IEEE Transactions on Knowledge and Data Engineering, 30 (7), 1352-1365 (2018). [DOI]
  51. Mamitsuka, H., Data Mining for Systems Biology: Methods and Protocols. (2nd Edition) Methods in Molecular Biology, 1807, Humana Press (part of Springer Nature) (2018). (Edited book) [DOI]
  52. Takahashi, K., duVerle, D. A., Yotsukura, S., Takigawa, I. and Mamitsuka, H., SiBIC: a Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining. Data Mining for Systems Biology: Methods and Protocols (2nd Edition), Methods in Molecular Biology, 1807, Chapter 8, 95-111 (2018). [DOI]  [pubmed]
  53. Deng, J., Yuan, Q., Mamitsuka, H. and Zhu, S., DrugE-Rank: Predicting Drug-target Interactions by Learning to Rank. Data Mining for Systems Biology: Methods and Protocols (2nd Edition), Methods in Molecular Biology, 1807, Chapter 14, 195-202 (2018). [DOI]  [pubmed]
  54. Peng, S., Mamitsuka, H. and Zhu, S., MeSHLabeler and DeepMeSH: Recent Progress in Large-scale MeSH Indexing. Data Mining for Systems Biology: Methods and Protocols (2nd Edition), Methods in Molecular Biology, 1807, Chapter 15, 203-209 (2018). [DOI]  [pubmed]
  55. Karasuyama, M. and Mamitsuka, H., Factor Analysis on a Graph. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS 2018) (JMLR Workshop and Conference Proceedings (PMLR), 84), 1117-1126 (2018). [Abstract]
  56. Yamada, M., Lian, W., Goyal, A., Chen, J., Wimalawarne, K., Kahn, S., Kaski, S., Mamitsuka H. and Chang, Y., Convex Factorization Machine for Toxicogenomics Prediction. Proceedings of the Twenty-third ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2017), 1215-1224 (2017). [DOI]
  57. Karasuyama, M. and Mamitsuka, H., Adaptive Edge Weighting for Graph-Based Learning Algorithms. Machine Learning, 106 (2), 307-335 (2017). [DOI]
  58. Takigawa, I. and Mamitsuka, H., Generalized Sparse Learning of Linear Models over the Complete Subgraph Feature Set. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39 (3), 617-624 (2017). [DOI]  [pubmed]
  59. Gönen, M., Weir, B. A., Cowley, G. S., Vazquez, F., Guan, Y., Jaiswal, A., Karasuyama, M., Uzunangelov, V., Wang, T., Tsherniak, A., Howell, S., Marbach, D., Hoff, B., Norman, T. C., Airola, A., Bivol, A., Bunte, K., Carlin, D., Chopra, B., Deran, A., Ellrott, K., Gopalacharyulu, P., Graim, K., Kaski, S., Khan, S. A., Newton, Y., Ng, S., Pahikkala, T., Paull, E., Sokolov, A., Tang, H., Tang, J., Wennerberg, K., Xie, Y., Zhan, X., Zhu, F., Broad-DREAM Community, Aittokallio, T., Mamitsuka, H., Stuart, J. M., Boehm, J., Root, D., Xiao, G., Stolovitzky, G., Hahn, W. C. and Margolin, A. A., A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines. Cell Systems, 5 (5), 485-497 (2017). [DOI]  [pubmed]
  60. Yotsukura, S., Karasuyama, M., Takigawa, I. and Mamitsuka, H., Exploring Phenotype Patterns of Breast Cancer within Somatic Mutations. Briefings in Bioinformatics 18 (4), 619-633 (2017). [DOI]  [pubmed]
  61. 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]
  62. 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]
  63. 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]
  64. 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, 85-94 (2016). [PDF]
  65. 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]
  66. 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]
  67. 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]
  68. 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]
  69. 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]
  70. 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]
  71. 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]
  72. 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]
  73. 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]
  74. 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]
  75. 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]
  76. 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]
  77. 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]
  78. 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]
  79. 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]
  80. 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]
  81. 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]
  82. 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]
  83. 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]
  84. 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]
  85. 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]
  86. 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]
  87. 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]
  88. 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]
  89. 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)]
  90. 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]
  91. 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]
  92. 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]
  93. 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]
  94. 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]
  95. 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]
  96. 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]
  97. 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]
  98. Mamitsuka, H., DeLisi, C., and Kanehisa, M., Data Mining for Systems Biology: Methods and Protocols. Methods in Molecular Biology, 939 (2013). (Edited book) [DOI]
  99. 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]
  100. 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]
  101. 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]
  102. Mamitsuka, H., Mining from Protein-Protein Interactions. WIREs Data Mining and Knowledge Discovery, 2 (5), 400-410 (2012). (Invited Review Paper) [DOI]
  103. 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]
  104. 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]
  105. 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]
  106. 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]
  107. Shiga, M. and Mamitsuka, H., Efficient Semi-Supervised Learning on Locally Informative Multiple Graphs. Pattern Recognition, 45 (3), 1035-1049 (2012). [DOI]
  108. 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]
  109. 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]
  110. Nguyen, C. H. and Mamitsuka, H., Discriminative Graph Embedding for Label Propagation. IEEE Transactions on Neural Networks, 22 (9), 1395-1405 (2011). [DOI]  [pubmed]
  111. Nguyen, C. H. and Mamitsuka, H., Kernels for Link Prediction with Latent Feature Models. Lecture Notes in Computer Science, 6912 (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]
  112. 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]
  113. duVerle, D., Ono, Y., Sorimachi, H. and Mamitsuka, H., Calpain Cleavage Prediction Using Multiple Kernel Learning. PLoS One, 6 (5), e19035 (2011). [DOI]  [pubmed]
  114. 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]
  115. 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]
  116. Mamitsuka, H., Glycoinformatics: Data Mining-based Approaches. Chimia, 65 (1/2), 10-13 (2011). (Invited Review Paper) [DOI]  [pubmed]
  117. 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]
  118. Takigawa, I. and Mamitsuka, H., Efficiently Mining d-Tolerance Closed Frequent Subgraphs. Machine Learning, 82 (2), 95-121 (2011). [DOI]
  119. 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]
  120. Hancock, T., Takigawa, I. and Mamitsuka, H., Mining Metabolic Pathways through Gene Expression. Bioinformatics, 26 (17), 2128-2135 (2010). [DOI]  [pubmed]
  121. 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]
  122. 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]
  123. Hancock, T. and Mamitsuka, H., A Markov Classification Model for Metabolic Pathways. Algorithms for Molecular Biology, 5, 10 (2010). [DOI]  [pubmed]
  124. 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]
  125. 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]
  126. 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]
  127. 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]
  128. 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]
  129. 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]
  130. Zhu, S., Zeng, J. and Mamitsuka, H., Enhancing MEDLINE Document Clustering by Incorporating MeSH Semantic Similarity. Bioinformatics, 25 (15), 1944-1951 (2009). [DOI]  [pubmed]
  131. 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]
  132. 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]
  133. 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]
  134. 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]
  135. 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]
  136. 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]
  137. 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]
  138. 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]
  139. 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]
  140. 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]
  141. 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]
  142. 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]
  143. Mamitsuka, H., Informatic Innovations in Glycobiology: Relevance to Drug Discovery. Drug Discovery Today, 13 (3/4), 118-123 (2008). (Invited Review Paper) [DOI]  [pubmed]
  144. 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]
  145. 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]
  146. 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]
  147. 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]
  148. 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]
  149. 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]
  150. 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]
  151. 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]
  152. Mamitsuka, H., Selecting Features in Microarray Classification Using ROC Curves. Pattern Recognition, 39 (12), 2393-2404 (2006). [DOI]
  153. 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]
  154. 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]
  155. 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]
  156. 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]
  157. 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]
  158. 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]
  159. 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]
  160. Mamitsuka, H., Finding the Biologically Optimum Alignment of Multiple Sequences. Artificial Intelligence in Medicine, 35 (1), 9-18 (2005). [DOI]  [pubmed]
  161. 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]
  162. 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]
  163. 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]
  164. 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]
  165. Mamitsuka, H., Efficient Unsupervised Mining from Noisy Co-occurrence Data. New Mathematics and Natural Computation, 1(1), 173-193 (2005). [DOI]
  166. 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]
  167. 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]
  168. 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]
  169. 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]
  170. 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]
  171. 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]
  172. 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]
  173. 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]
  174. 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]
  175. 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]
  176. 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]
  177. 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]
  178. 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]
  179. 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]
  180. 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]
  181. 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 paper site]
  182. 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]
  183. 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]
  184. 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]
  185. 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]
  186. 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]
  187. 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]
  188. 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]
  189. 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).
  190. 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]
  191. 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]
  192. 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]
  193. 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]
  194. Abe, N. and Mamitsuka, H., Predicting Protein Secondary Structures Using Stochastic Tree Grammars. Machine Learning, 29 (2-3), 275-301 (1997). [DOI]
  195. 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]
  196. Mamitsuka, H., A Learning Method of Hidden Markov Models for Sequence Discrimination. Journal of Computational Biology, 3 (3), 361-373 (1996). [DOI]  [pubmed]
  197. Mamitsuka, H., Representing Inter-Residue Dependencies in Protein Sequences with Probabilistic Networks. Computer Applications in the Biosciences, 11 (4), 413-422 (1995). [DOI]  [pubmed]
  198. 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]
  199. 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 paper site ]  [pubmed]
  200. 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]
  201. 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]
  202. 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: Sep 09,2024