Publications
(Since 2002, sorted reverse chronologically)

Refereed Publications

Learning Subtree Pattern Importance for Weisfeiler-Lehman based Graph Kernels.
Nguyen, D. H., Nguyen, C. H. and Mamitsuka, H.
To appear in Machine Learning, 2021. (Special Issue of the ECML/PKDD 2021 Journal Track)
[DOI].


Learning on Hypergraphs with Sparsity.
Nguyen, C. H. and Mamitsuka, H.
To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
[DOI].


DeepGraphGO: Graph Neural Net for Large-scale, Multispecies Protein Function Prediction.
You, R., Yao, S., Mamitsuka H. and Zhu, S.
To appear in Proceedings of the 29th International Conference on Intelligent Systems for Molecular Biology (ISMB/ECCB 2021) (Bioinformatics), Virtual Event, 2021, Oxford University Press.
[DOI].


DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction.
Güvenç, B. P., Kaski, S. and Mamitsuka, H.
To appear in IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021.
[DOI].


HPOFiller: Identifying Missing Protein-phenotype Associations by Graph Convolutional Network.
Liu, L, Mamitsuka, H. and Zhu, S.
To appear in Bioinformatics, 2021.
[DOI].


XGSEA: CROSS-species Gene Set Enrichment Analysis via Domain Adaptation.
Cai, M., Nguyen, C. H., Mamitsuka, H. and Li, L.
To appear in Briefings in Bioinformatics, 2021.
[DOI].


Improving Drug Response Prediction by Integrating Multiple Data Sources: Matrix Factorization, Kernel and Network-based Approaches.
Güvenç, B. P., Mamitsuka, H. and Kaski, S.
Briefings in Bioinformatics, 22 (1), 346-359, 2021.
[DOI].


A Survey on Adverse Drug Reaction Studies: Data, Tasks, and Machine Learning Methods.
Nguyen, D. A., Nguyen, C. H. and Mamitsuka, H.
Briefings in Bioinformatics, 22 (1), 164-177, 2021.
[DOI].


Eukaryotic Virus Composition Can Predict the Efficiency of Carbon Export in the Global Ocean.
Kaneko, H., Blanc-Mathieu, R., Endo, H., Chaffron, S., Delmont, T. O., Gaia, M., Henry, N., Hernandez-Velazquez, R., Nguyen, C.-H., Mamitsuka, H., Forterre, P., Jaillon, O., de Vargas, C., Sullivan, M. B., Suttle, C. A., Guidi, L. and Ogata, H.
iScience, 24 (1), 102002, 2021.
[DOI].


Reshaped Tensor Nuclear Norms for Higher Order Tensor Completion.
Wimalawarne, K. and Mamitsuka, H.
Machine Learning, 110 (3), 507-531, 2021.
[DOI].


BERTMeSH: Deep Contextual Representation Learning for Large-scale High-performance MeSH Indexing with Full Text.
Yui, R., Liu, Y., Mamitsuka, H. and Zhu, S.
Bioinformatics, 37 (5), 684-692, 2021.
[DOI].


HPOLabeler: Improving Prediction of Human Protein-phenotype Associations by Learning to Rank.
Liu, L., Huang, X., Mamitsuka, H. and Zhu, S.
Bioinformatics, 36 (14), 4180-4188, 2020.
[DOI].


Scalable Probabilistic Matrix Factorization with Graph-Based Priors.
Strahl, J., Peltonen, J., Mamitsuka, H. and Kaski, S.
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2020), 34, 5851-5858, New York, USA, 2020.
[DOI].


Efficiently Enumerating Substrings with Statistically Significant Frequencies of Locally Optimal Occurrences in Gigantic String.
Nakamura, A., Takigawa, I. and Mamitsuka, H.
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2020), 34, 5240-5247, New York, USA, 2020.
[DOI].


FullMeSH: Improving Large-Scale MeSH Indexing with Full Text.
Dai, S., You, R., Lu, Z., Huang, X., Mamitsuka, H. and Zhu, S.
Bioinformatics, 36 (5), 1533-1541, 2020.
[DOI].


Scaled Coupled Norms and Coupled Higher Order Tensor Completion.
Wimalawarne, K., Yamada, M. and Mamitsuka, H.
Neural Computation, 32 (2), 447-484, 2020.
[DOI].


Recent Advances and Prospects of Computational Methods for Metabolite Identification: A Review with Emphasis on Machine Learning Approaches.
Nguyen, D. H., Nguyen, C. H. and Mamitsuka, H.
Briefings in Bioinformatics, 20 (6), 2028-2043, 2019.
[DOI].


AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification.
You, R., Dai, S., Zhang, Z., Mamitsuka, H. and Zhu, S.
Proceedings of the 33rd Annual Conference on Neural Information Processing Systems (NeurIPS 2019), 5820-5830, Vancouver, Canada, 2019.
[NeurIPS paper site].


Multiplicative Sparse Feature Decomposition for Efficient Multi-View Multi-Task Learning.
Sun, L., Nguyen, C. H. and Mamitsuka, H.
Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), 3605-3512, Macao, China, 2019.
[DOI].


Fast and Robust Multi-View Multi-Task Learning via Group Sparsity.
Sun, L., Nguyen, C. H. and Mamitsuka, H.
Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), 3499-3505, Macao, China, 2019.
[DOI].


NetGO: Improving Large-scale Protein Function Prediction with Massive Network Information.
You, R., Yao, S., Xiong, Y., Huang, X., Sun, F., Mamitsuka, H. and Zhu, S.
Nucleic Acids Research, 47, W379-W387, 2019.
[DOI].


ADAPTIVE: leArning DAta-dePendenT, concIse molecular VEctors for fast, accurate metabolite identification from tandem mass spectra.
Nguyen, D. H., Nguyen, C. H. and Mamitsuka, H.
Proceedings of the 27th International Conference on Intelligent Systems for Molecular Biology (ISMB/ECCB 2019) (Bioinformatics, 35 (14)), i64-i72, Basel, Switzerland, 2019, Oxford University Press.
[DOI].


Modelling GxE with Historical Weather Information Improves Genomic Prediction in New Environments.
Gillberg, J., Marttinen, P., Mamitsuka, H. and Kaski, S.
Bioinformatics, 35 (20), 4045-4052, 2019.
[DOI].


A Metropolis-Hastings Sampling of Subtrees in Graphs.
Eid, A., Mamitsuka, H. and Wicker, N.
Austrian Journal of Statistics, 48 (5), 17-33, 2019.
[DOI].


A p-Laplacian Random Walk: Application to Video Games.
Wicker, N., Nguyen, C. H. and Mamitsuka, H.
Austrian Journal of Statistics, 48 (5), 11-16, 2019.
[DOI].


Efficient Convex Completion of Coupled Tensors using Coupled Nuclear Norms.
Wimalawarme, K. and Mamitsuka, H.
Proceedings of the Thirty-Second Annual Conference on Neural Information Processing Systems (NeurIPS 2018), 6902-6910, Montréal, Canada, 2018.
[NeurIPS paper site].


AiProAnnotator: Low-rank Approximation with Network Side Information for High-performance, Large-scale Human Protein Abnormality Annotator.
Gao, J., Shuwei, Y., Mamitsuka, H. and Zhu, S.
Proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018), 13-20, Madrid, Spain, December, 2018.
(Best student paper)
[DOI].


Convex Coupled Matrix and Tensor Completion.
Wimalawarme, K., Yamada, M. and Mamitsuka, H.
Neural Computation, 30 (11), 3095-3127, 2018.
[DOI].


SIMPLE: Sparse Interaction Model over Peaks of MoLEcules for Fast, Interpretable Metabolite Identification from Tandem Mass Spectra.
Nguyen, D. H., Nguyen, C. H. and Mamitsuka, H.
Proceedings of the 26th International Conference on Intelligent Systems for Molecular Biology (ISMB 2018) (Bioinformatics, 34 (13), 2018), i323-i332, Chicago, IL, USA, 2018, Oxford University Press.
[DOI].


GOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank.
You, R., Zhang, Z., Xiong, Y., Sun, F., Mamitsuka, H. and Zhu, S.
Bioinformatics, 34 (14), 2465-2478, 2018.
[DOI].


Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data.
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.
IEEE Transactions on Knowledge and Data Engineering, 30 (7), 1352-1365, 2018.
[DOI].


Factor Analysis on a Graph.
Karasuyama, M. and Mamitsuka, H.
Proceedings of the Twenty-first International Conference on Artificial Intelligence and Statistics (AISTATS 2018) (JMLR Workshop and Conference Proceedings (PMLR),84), 1117-1126, Playa Blanca, Lanzarote, Canary Islands, April, 2018.
[PMLR paper site].


Convex Factorization Machine for Toxicogenomics Prediction.
Yamada, M., Lian, W., Goyal, A., Chen, J., Wimalawarne, K., Kahn, S., Kaski, S., Mamitsuka H. and Chang, Y.
Proceedings of the Twenth-third ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2017), 1215-1224, Halifax, Nova Scotia, Canada, August 2017, ACM Press.
[DOI].


Adaptive Edge Weighting for Graph-Based Learning Algorithms.
Karasuyama, M. and Mamitsuka, H.
Machine Learning, 106 (2), 307-335, 2017.
[DOI].


Generalized Sparse Learning of Linear Models over the Complete Subgraph Feature Set.
Takigawa, I. and Mamitsuka, H.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 39 (3), 617-624, 2017.
[DOI].


A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines.
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.
Cell Systems, 5 (5), 485-497, 2017.
[DOI].


Exploring Phenotype Patterns of Breast Cancer within Somatic Mutations.
Yotsukura, S., Karasuyama, M., Takigawa, I. and Mamitsuka, H.
Briefings in Bioinformatics, 18 (4), 619-633, 2017.
[DOI].


Computational Recognition for Long Non-coding RNA (lncRNA): Software and Databases.
Yotsukura, S., duVerle, D., Hancock, T., Natsume-Kitatani, Y. and Mamitsuka, H.
Briefings in Bioinformatics, 18 (1), 9-27, 2017.
[DOI].


A Robust Convex Formulations for Ensemble Clustering.
Gao, J., Yamada, M., Kaski, S., Mamitsuka, H. and Zhu, S.
Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), 1476-1482, New York City, NY, USA, July, 2016.
[IJCAI paper site].


New Resistance Distances with Global Information on Large Graphs.
Nguyen, C. H. and Mamitsuka, H.
Proceedings of the Nineteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2016) (JMLR Workshop and Conference Proceedings,51), 639-647, Cadiz, Spain, May, 2016.
[PMLR paper site].


Some Properties of a Dissimilarity Measure for Labeled Graphs.
Wicker, N., Nguyen, C. H. and Mamitsuka, H.
Publications Mathématiques de Besançon: Algèbre et Théorie des Nombres, 85-94, 2016.
[PDF].


DrugE-Rank: Improving Drug-Target Interaction Prediction of New Candidate Drugs or Targets by Ensemble Learning to Rank.
Yuan, Q.-J., Gao, J., Wu, D., Zhang, S., Mamitsuka, H. and Zhu, S.
Proceedings of the 24th International Conference on Intelligent Systems for Molecular Biology (ISMB 2016) (Bioinformatics, 32 (12), 2016), i18-i27, Orlando, FL, USA, 2016, Oxford University Press.
[DOI].


DeepMeSH: Deep Semantic Representation for Improving Large-scale MeSH Indexing.
Peng, S., You, R., Wang, H., Zhai, C., Mamitsuka, H. and Zhu, S.
Proceedings of the 24th International Conference on Intelligent Systems for Molecular Biology (ISMB 2016) (Bioinformatics, 32 (12), 2016), i70-i79, Orlando, FL, USA, 2016, Oxford University Press.
[DOI].


NMRPro: An Integrated Web Component for Interactive Processing and Visualization of NMR Spectra.
Mohamed, A., Nguyen, C. H. and Mamitsuka, H.
Bioinformatics. 32 (13), 2067-2068, 2016.
[DOI].


Gene-proximity Models for Genome-Wide Association Studies.
Johnston, I., Hancock, T., Mamitsuka, H. and Carvalho, L.
Annals of Applied Statistics, 10 (3), 1217-1244, 2016.
[DOI].


Classification of Promoters based on the Combination of Core Promoter Elements Exhibits Different Histone Modification Patterns.
Natsume-Kitatani, Y. and Mamitsuka, H.
PLoS One, 11 (3), e0151917, 2016.
[DOI].


Predictions of Cleavability of Calpain Proteolysis by Quantitative Structure-Activity Relationship Analysis Using Newly Determined Cleavage Sites and Catalytic Efficiencies of an Oligopeptide Array.
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.
Molecular and Cellular Proteomics, 15, 1262-1280, 2016.
[DOI].


Mining Approximate Patterns with Frequent Locally Optimal Occurrences.
Nakamura, A., Takigawa, I., Tosaka, H., Kudo, M. and Mamitsuka, H.
Discrete Applied Mathematics, 200, 123-152, 2016.
[DOI].


A Bioinformatics Approach for Understanding Genotype-phenotype Correlation in Breast Cancer.
Yotsukura, S., Karasuyama, M., Takigawa, I. and Mamitsuka, H.
Big Data Analytics in Genomics, Chapter 13, 397-428, 2016.
[DOI].


Current Status and Prospects of Computational Resources for Natural Product Dereplication: A Review.
Mohamed, A., Nguyen, C. H., and Mamitsuka, H.
Briefings in Bioinformatics, 17(2), 309-312, 2016.
[DOI].


Instance-wise Weighted Nonnegative Matrix Factorization for Aggregating Partitions with Locally Reliable Clusters.
Zheng, X., Zhu, S., Gao, J. and Mamitsuka, H.
Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), 4091-4097, Buenos Aires, Argentina, July, 2015.
[PDF].


MeSHLabeler: Improving the Accuracy of Large-scale MeSH indexing by Integrating Diverse Evidence.
Liu, K., Peng, S., Wu, J., Zhai, C., Mamitsuka H. and Zhu S.
Proceedings of the 23rd International Conference on Intelligent Systems for Molecular Biology (ISMB/ECCB 2015), (Bioinformatics, 31(12), 2015), i339-i347, Dublin, Ireland, July, 2015, Oxford University Press.
[DOI].


MeSHSim: An R/Bioconductor Package for Measuring Semantic Similarity over MeSH Headings and MEDLINE Documents.
Zhou, J., Shui, Y., Peng, S., Li, X., Mamitsuka, H., Zhu, S.
Journal of Bioinformatics and Computational Biology, 13(6), 1542002, 2015.
[DOI].


In Silico Predictions of Human Skin Permeability using Nonlinear Quantitative Structure-Property Relationship Models.
Baba, H., Takahara, J. and Mamitsuka, H.
Pharmaceutical Research, 32(7), 2360-2371, 2015.
[DOI].


Non-negative Matrix Factorization with Auxiliary Information on Overlapping Groups.
Shiga, M. and Mamitsuka, H.
IEEE Transactions on Knowledge and Data Engineering, 27(6), 1615-1628, 2015.
[DOI].


BMExpert: Mining MEDLINE for Finding Experts in Biomedical Domains Based on Language Model.
Wang, B., Chen, X., Mamitsuka, H. and Zhu, S.
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12(6), 1286-1294, 2015.
[DOI].


Evaluation of Serum-based Cancer Biomarkers: A Brief Review from a Clinical and Computational Viewpoint.
Yotsukura, S. and Mamitsuka, H.
Critical Reviews in Oncology/Hematology, 95(2), 103-115, 2015.
[DOI].


NetPathMiner:R/Bioconductor Package for Network Path Mining through Gene Expression.
Mohamed, A., Hancock, T., Nguyen, C. H. and Mamitsuka, H.
Bioinformatics, 30(21), 3139-3141, 2014.
[DOI].


Detecting Differentially Coexpressed Genes from Labeled Expression Data:A Brief Review.
Kayano, M., Shiga, M. and Mamitsuka, H.
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11(1), 154-167, 2014.
[DOI].


Selecting Graph Cut Solutions via Global Graph Similarity.
Nguyen, C. H., Wicker, N. and Mamitsuka, H.
IEEE Transactions on Neural Networks and Learning Systems, 25(7), 1407-1412, 2014.
[DOI].


Similarity-based Machine Learning Methods for Predicting Drug-target Interactions: A Brief Review.
Ding, H., Takigawa, I., Mamitsuka, H. and Zhu, S.
Briefings in Bioinformatics, 15(5), 737-747, 2014.
[DOI].


Manifold-based Similarity Adaptation for Label Propagation.
Karasuyama, M. and Mamitsuka, H.
Proceedings of the Twenty-Seventh Annual Conference on Neural Information Processing Systems (NIPS 2013), 1547-1555, Lake Tahoe, NV, USA, December, 2013.
[NIPS Paper site].


SiBIC: A Web Server for Generating Gene Set Networks Based on Biclusters Obtained by Maximal Frequent Itemset Mining.
Takahashi, K., Takigawa, I. and Mamitsuka, H.
PLoS One, 8(12), e82890, 2013.
[DOI].


Multiple Graph Label Propagation by Sparse Integration.
Karasuyama, M. and Mamitsuka, H.
IEEE Transactions on Neural Networks and Learning Systems, 24(12), 1999-2012, 2013.
[DOI].


Collaborative Matrix Factorization with Multiple Similarities for Predicting Drug-target Interactions.
Zheng, X., Ding, H., Mamitsuka, H. and Zhu, S.
Proceedings of the Nineteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2013), 1025-1033, Chicago, IL, USA, August 2013, ACM Press.
[DOI, PDF (Preprints)].


Variational Bayes Co-clustering with Auxiliary Information.
Shiga, M. and Mamitsuka, H.
Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering (MultiClust 2013), Article No. 5, Chicago, IL, USA, August, 2013.
[DOI].


Fast Algorithms for Finding a Minimum Repetition Representation of Strings and Trees.
Nakamura, A., Saito, T., Takigawa, I., Kudo, M. and Mamitsuka, H.
Discrete Applied Mathematics, 161(10-11), 1556-1575, 2013.
[DOI].


Efficient Semi-supervised MEDLINE Document Clustering with MeSH Semantic and Global Content Constraints.
Gu, J., Feng, W., Zeng, J., Mamitsuka, H. and Zhu, S.
IEEE Transactions on Cybernetics, 43(4), 1265-1276, 2013.
[DOI].


A New Dissimilarity Measure for Comparing Labeled Graphs.
Wicker, N., Nguyen, C. H. and Mamitsuka, H.
Linear Algebra and its Applications, 438(5), 2331-2338, 2013.
[DOI].


Integrated View of the Human Chromosome X-centric Proteome Project
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.
Journal of Proteome Research, 12(1), 58-61, 2013.
[DOI].


Graph Mining: Procedure, Application to Drug Discovery and Recent Advance
Takigawa, I. and Mamitsuka, H.
Drug Discovery Today, 18 (1/2), 50-57, 2013.
Invited Review Paper.
[DOI].


Latent Feature Kernels for Link Prediction on Sparse Graphs.
Nguyen, C. H. and Mamitsuka, H.
IEEE Transactions on Neural Networks and Learning Systems, 23(11), 1793-1804, 2012.
[DOI].


Boosted Network Classifiers for Local Feature Selection.
Hancock, T. and Mamitsuka, H.
IEEE Transactions on Neural Networks and Learning Systems, 23(11), 1767-1778, 2012.
[DOI].


Understanding the Substrate Specificity of Conventional Calpains.
Sorimachi, H., Mamitsuka, H. and Ono, Y.
Biological Chemistry, 393(9), 853-871, 2012.
Invited Review Paper.
[DOI].


Mining from Protein-Protein Interactions.
Mamitsuka, H.
WIREs Data Mining and Knowledge Discovery, 2(5), 400-410, 2012.
Invited Review Paper.
[DOI].


Identifying Neighborhoods of Coordinated Gene Expression and Metabolite Profiles.
Hancock, T, Wicker, N., Takigawa, I. and Mamitsuka, H.
PLoS One, 7(2), e31345, 2012.
[DOI].


A Variational Bayesian Framework for Clustering with Multiple Graphs.
Shiga, M. and Mamitsuka, H.
IEEE Transactions on Knowledge and Data Engineering, 24(4), 577-590, 2012.
[DOI].


TEPITOPEpan: Extending TEPITOPE for Peptide Binding Prediction Covering over 700 HLA-DR Molecules.
Zhang, L, Chen, Y., Wong, H.-S., Zhou, S., Mamitsuka, H. and Zhu, S.
PLoS One, 7(2), e30483, 2012.
[DOI].


Toward More Accurate Pan-Specific MHC-Peptide Binding Prediction: A Review of Current Methods and Tools.
Zhang, L, Udaka, K, Mamitsuka, H. and Zhu, S.
Briefings in Bioinformatics, 13(3), 350-364, 2012.
[DOI].


A Review of Statistical Methods for Prediction of Proteolytic Cleavage.
duVerle, D. and Mamitsuka, H.
Briefings in Bioinformatics, 13(3), 337-349, 2012.
[DOI].


Machine Learning Sequence Classification Techniques: Application to Cysteine Protease Cleavage Prediction.
duVerle, D. and Mamitsuka, H.
Current Bioinformatics, 7(4), 415-523, 2012.
Invited Review Paper.


Efficient Semi-Supervised Learning on Locally Informative Multiple Graphs.
Shiga, M. and Mamitsuka, H.
Pattern Recognition, 45(3), 1035-1049, 2012.
[DOI].


Genome-wide Integration on Transcription Factors, Histone Acetylation and Gene Expression Reveals Genes Co-regulated by Histone Modification Patterns.
Natsume-Kitatani, Y., Shiga, M. and Mamitsuka, H.
PLoS One, 6(7), e22281, 2011.
[DOI].


Discriminative Graph Embedding for Label Propagation.
Nguyen, C. H. and Mamitsuka, H.
IEEE Transactions on Neural Networks, 22(9), 1395-1495, 2011.
[DOI].


Clustering Genes with Expression and Beyond.
Shiga, M. and Mamitsuka, H.
WIREs Data Mining and Knowledge Discovery, 1(6), 496-511, 2011.
Invited Review Paper.
[DOI].


Kernels for Link Prediction with Latent Feature Models.
Nguyen, C. H. and Mamitsuka, H.
Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2011), Part II (Lecture Notes in Artificial Intelligence, 6912), 517-532, Athens, Greece, September, 2011, Springer.
[DOI].


Calpain Cleavage Prediction Using Multiple Kernel Learning.
duVerle, D., Ono, Y., Sorimachi, H. and Mamitsuka, H.
PLoS One, 6(5), e19035, 2011.
[DOI].


ROS-DET: Robust Detector of Switching Mechanisms in Gene Expression.
Kayano, M., Takigawa, I., Shiga, M., Tsuda, K. and Mamitsuka, H.
Nucleic Acids Research, 39 (11), e74, 2011.
[DOI].


Ensemble Approaches for Improving HLA Class I-peptide Binding Prediction.
Hu, X., Mamitsuka, H. and Zhu, S.
Journal of Immunological Methods, 374(1/2), 47-52, 2011.
[DOI].


Mining Significant Substructure Pairs for Interpreting Polypharmacology in Drug-Target Network.
Takigawa, I., Tsuda, K. and Mamitsuka, H.
PLoS One, 6(2), e16999, 2011.
[DOI].


Efficiently Mining δ-Tolerance Closed Frequent Subgraphs.
Takigawa, I. and Mamitsuka, H.
Machine Learning, 82(2), 95-121, 2011.
[DOI].


A Spectral Approach to Clustering Numerical Vectors as Nodes in a Network.
Shiga, M., Takigawa, I. and Mamitsuka, H.
Pattern Recognition, 44(2), 236-251, 2011.
[DOI].


Mining Metabolic Pathways through Gene Expression.
Hancock, T., Takigawa, I. and Mamitsuka, H.
Bioinformatics, 26(17), 2128-2135, 2010.
[DOI].


MetaMHC: A Meta Approach to Predict Peptides Binding to MHC Molecules.
Hu, X., Zhou, W., Udaka, K., Mamitsuka, H. and Zhu, S.
Nucleic Acids Research, 38, W474-W479, 2010.
[DOI].


Boosted Optimization for Network Classification.
Hancock, T. and Mamitsuka, H.,
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010) (JMLR: Workshop and Conference Proceedings, Vol. 9) , 305-312, Sardinia, Italy, May 2010, MIT Press.
[PMLR paper site].


A Markov Classification Model for Metabolic Pathways.
Hancock, T. and Mamitsuka, H.
Algorithms for Molecular Biology, 5 (1), 10, 2010.
Special Issue: Selected papers from WABI 2009
[DOI].


Algorithms for Finding a Minimum Repetition Representation of a String
Nakamura, A., Saito, T., Takigawa, I., Mamitsuka, H. and Kudo, M.
Proceedings of the Seventeenth Symposium on String Processing and Information Retrieval (SPIRE 2010, Lecture Notes in Computer Science, 6393), 185-190, Los Cabos, Mexico, October, 2010.
[DOI].


Variational Bayes Learning over Multiple Graphs.
Shiga, M. and Mamitsuka, H.
Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010), 166-171, Kitilla, Finland, September, 2010.
[DOI].


On Network-based Kernel Methods for Protein-Protein Interactions with Applications in Protein Functions.
Li, L., Ching, W-K., Chan, Y-M. and Mamitsuka, H.
Journal of Systems Science and Complexity 23(5), 917-930, 2010
[DOI].


TAP Hunter: A SVM-based System for Predicting TAP Ligands using Local Description of Amino Acid Sequence.
Lam, T. H., Mamitsuka, H., Ren, E. C., and Tong, J. C.
Immunome Research, 6 (Suppl. 1), S6, 2010.
[DOI].


Efficiently Finding Genome-wide Three-way Gene Interactions from Transcript- and Genotype-Data.
Kayano, M., Takigawa, I., Shiga, M., Tsuda, K. and Mamitsuka, H.
Bioinformatics, 25 (21), 2735-2743, 2009.
[DOI].


Enhancing MEDLINE Document Clustering by Incorporating MeSH Semantic Similarity.
Zhu, S., Zeng, J. and Mamitsuka, H.
Bioinformatics, 25 (15), 1944-1951, 2009.
[DOI].


Field Independent Probabilistic Model for Clustering Multi-Field Documents.
Zhu, S., Takigawa, I., Zeng, J. and Mamitsuka, H.
Information Processing and Management, 45 (5), 555-570, 2009.
[DOI, PDF (Advance access)].


HAMSTER: Visualizing Microarray Experiments as a Set of Minimum Spanning Trees.
Wan, R., Kiseleva, L., Harada, H., Mamitsuka, H. and Horton, P.
Source Code for Biology and Medicine, 4, 8, 2009.
[DOI].


A Markov Classification Model for Metabolic Pathways.
Hancock, T. and Mamitsuka, H.
Proceedings of the Ninth Workshop on Algorithms in Bioinformatics (WABI 2009), (Lecture Notes in Bioinformatics, 5724), 121-132 , Philadelphia, PA, USA, September, 2009, Springer-Verlag.
[DOI].


Efficient Probabilistic Latent Semantic Analysis through Parallelization.
Wan, R., Vo, N. A. and Mamitsuka, H.
Proceedings of the Fifth Asia Information Retrieval Symposium (AIRS2009, Lecture Notes in Computer Science, 5839), 432-443, Sapporo, Japan, October, 2009, Springer-Verlag.
[DOI].


A Study of Network-based Kernel Methods on Protein-Protein Interaction for Protein Functions Prediction.
Ching, W-K., Li, L., Chan, Y-M. and Mamitsuka, H.
Proceedings of the Third International Symposium on Optimization and Systems Biology (OSB 2009, Lecture Notes in Operations Research, 11), 25-32, Zhangjiajie, China, September, 2009, APORC Press
[PDF (APORC)].


Discovering Network Motifs in Protein Interaction Networks.
Wan, R. and Mamitsuka, H.
Biological Data Mining in Protein Interaction Networks, Chapter 8, 117-143, 2009, IGI Global.
[Bibtex].


Mining Significant Tree Patterns in Carbohydrate Sugar Chains.
Hashimoto, K., Takigawa, I., Shiga, M., Kanehisa, M. and Mamitsuka, H.
Proceedings of the Seventh European Conference on Computational Biology (ECCB 2008), (Bioinformatics, 24 (16), 2008), i167-i173, Cagliari, Sardinia-Italy, September, 2008, Oxford University Press.
[DOI].


A New Efficient Probabilistic Model for Mining Labeled Ordered Trees Applied to Glycobiology.
Hashimoto, K., Aoki-Kinoshita, K. F., Ueda, N., Kanehisa, M. and Mamitsuka, H.
ACM Transactions on Knowledge Discovery from Data, 2 (1), Article No. 6, 2008.
[DOI].


Probabilistic Path Ranking Based on Adjacent Pairwise Coexpression for Metabolic Transcripts Analysis.
Takigawa, I. and Mamitsuka, H.
Bioinformatics, 24 (2), 250-257, 2008.
[DOI].


Informatic Innovations in Glycobiology: Relevance to Drug Discovery.
Mamitsuka, H.
Drug Discovery Today, 13 (3/4), 118-123, 2008.
Invited Review Paper.
[DOI].


A Spectral Clustering Approach to Optimally Combining Numerical Vectors with a Modular Network.
Shiga, M., Takigawa, I. and Mamitsuka, H.,
Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2007), 647-656, San Jose, CA, USA, August 2007, ACM Press.
[DOI, PDF (Preprints)].


Annotating Gene Function by Combining Expression Data with a Modular Gene Network.
Shiga, M., Takigawa, I. and Mamitsuka, H.
Proceedings of the Fifteenth International Conference on Intelligent Systems for Molecular Biology (ISMB/ECCB 2007), (Bioinformatics, 23 (13), 2007), i468-i478, Vienna, Austria, July, 2007, Oxford University Press.
[DOI].


A Hidden Markov Model-based Approach for Identifying Timing Differences in Gene Expression under Different Experimental Factors.
Yoneya, T. and Mamitsuka, H.
Bioinformatics, 23 (7), 842-849, 2007.
[DOI].


A Probabilistic Model for Clustering Text Documents with Multiple Fields.
Zhu, S., Takigawa, I., Zhang, S. and Mamitsuka, H.
Proceedings of the 29th European Conference on Information Retrieval (ECIR 2007, Lecture Notes in Computer Science, 4425), 331-342, Roma, Italy, April, 2007, Springer-Verlag.
[DOI].


Identification of Endocrine Disruptor Biodegradation by Integration of Structure-activity Relationship with Pathway Analysis.
Kadowaki, T., Wheelock, C. E., Adachi, T., Kudo, T., Okamoto, S., Tanaka, N., Tonomura, K., Tsujimoto, G., Mamitsuka, H., Goto, S. and Kanehisa, M.
Environmental Science & Technology, 41 (23), 7997-8003, 2007.
[DOI].


Selecting Features in Microarray Classification Using ROC Curves
Mamitsuka, H.
Pattern Recognition, 39 (12), 2393-2404, 2006.
[DOI].


Applying Gaussian Distribution-dependent Criteria to Decision Trees for High-Dimensional Microarray Data
Wan, R., Takigawa, I. and Mamitsuka, H.
Proceedings of 2006 VLDB Workshop on Data Mining in Bioinformatics (Lecture Notes in Bioinformatics, 4316), 40-49 , Seoul, Korea, September, 2006, Springer-Verlag.
[DOI].


A New Efficient Probabilistic Model for Mining Labeled Ordered Trees.
Hashimoto, K., Aoki-Kinoshita, K. F., Ueda, N., Kanehisa, M. and Mamitsuka, H.,
Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006), 177-186, Philadelphia, PA, USA, August 2006, ACM Press.
[DOI, PDF (Preprints)].


ProfilePSTMM: Capturing Tree-structure Motifs in Carbohydrate Sugar Chains.
Aoki, K. F., Ueda, N., Mamitsuka, H. and Kanehisa, M.
Proceedings of the Fourteenth International Conference on Intelligent Systems for Molecular Biology (ISMB 2006), (Bioinformatics, 22 (14), e25-e34), Fortaleza, Brazil, August, 2006, Oxford University Press.
[DOI].


Application of a New Probabilistic Model for Mining Implicit Associated Cancer Genes from OMIM and Medline.
Zhu, S., Okuno, Y., Tsujimoto, G. and Mamitsuka, H.
Cancer Informatics, 2, 361-371, 2006.
[Cancer Informatics, Bibtex].


Improving MHC Binding Peptide Prediction by Incorporating Binding Data of Auxiliary MHC Molecules.
Zhu, S., Udaka, K., Sidney, J., Sette, A., Aoki-Kinoshita, K. F. and Mamitsuka, H.
Bioinformatics, 22 (13), 1648-1655, 2006.
[DOI].


Query-Learning-Based Iterative Feature-Subset Selection for Learning from High-Dimensional Data Sets.
Mamitsuka, H.
Knowledge and Information Systems, 9 (1), 91-108, 2006.
[DOI].


A Probabilistic Model for Mining Implicit ``Chemical Compound - Gene'' Relations from Literature.
Zhu, S., Okuno, Y., Tsujimoto, G. and Mamitsuka, H.
Proceedings of the Fourth European Conference on Computational Biology (ECCB/JBI 2005), (Bioinformatics, 21, Supplement 2, ii245-ii251), Madrid, Spain, September, 2005, Oxford University Press.
[DOI].


Finding the Biologically Optimal Alignment of Multiple Sequences
Mamitsuka, H.
Artificial Intelligence in Medicine, 35 (1), 9-18, 2005.
[DOI].


Probabilistic Model for Mining Labeled Ordered Trees: Capturing Patterns in Carbohydrate Sugar Chains.
Ueda, N., Aoki-Kinoshita, K. F., Yamaguchi, A., Akutsu, T. and Mamitsuka, H.
IEEE Transactions on Knowledge and Data Engineering, 17 (8), 1051-1064, 2005.
[DOI].


Mining New Protein-Protein Interactions - Using a Hierarchical Latent-variable Model to Determine the Function of a Functionally Unknown Protein.
Mamitsuka, H.
IEEE Engineering in Medicine and Biology Magazine, 24 (3), 103-108, 2005.
[DOI].


Essential Latent Knowledge for Protein-Protein Interactions: Analysis by an Unsupervised Learning Approach.
Mamitsuka, H.
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2 (2), 119-130, 2005.
[DOI].


Efficient Unsupervised Mining from Noisy Co-occurrence Data.
Mamitsuka, H.
New Mathematics and Natural Computation, 1 (1), 173-193, 2005.
[DOI].


A Score Matrix to Reveal the Hidden Links in Glycans.
Aoki, K. F., Mamitsuka, H., Akutsu, T. and Kanehisa, M.
Bioinformatics, 21 (8), 1457-1463, 2005.
[DOI].


Cleaning Microarray Expression Data Using Markov Random Fields based on Profile Similarity.
Wan, R., Mamitsuka, H. and Aoki, K. F.
Proceedings of the Twentieth ACM Symposium on Applied Computing (SAC 2005), 206-207, Santa Fe, NM, USA, March, 2005, ACM Press.
[DOI].


The Evolutionary Repertoires of the Eukaryotic-type ABC Transporters in terms of the Phylogeny of ATP-binding Domains in Eukaryotes and Prokaryotes.
Igarashi, Y., Aoki, K. F., Mamitsuka, H., Kuma, K. and Kanehisa, M.
Molecular Biology and Evolution, 21 (11), 2149-2160, 2004.
[DOI].


Finding the Maximum Common Subgraph of a Partial k-Tree and a Graph with a Polynomially Bounded Number of Spanning Trees.
Yamaguchi, A., Aoki, K. F. and Mamitsuka, H.
Information Processing Letters, 92 (2), 57-63, 2004.
[DOI].


A Hierarchical Mixture of Markov Models for Finding Biologically Active Metabolic Paths using Gene Expression and Protein Classes.
Mamitsuka, H. and Okuno, Y.
Proceedings of the IEEE Computational Systems Bioinformatics Conference (CSB 2004), 341-352, Stanford, CA, USA, August, 2004, IEEE Computer Society Press.
[DOI, PDF, Bibtex].


Application of a New Probabilistic Model for Recognizing Complex Patterns in Glycans.
Aoki, K. F., Ueda, N., Yamaguchi, A., Kanehisa, M., Akutsu, T. and Mamitsuka, H.
Proceedings of the Twelfth International Conference on Intelligent Systems for Molecular Biology (ISMB/ECCB 2004), (Bioinformatics, 20, Supplement 1, i6-i14), Glasgow, UK, August, 2004, Oxford University Press.
[DOI].


KCaM (KEGG Carbohydrate Matcher): A Software Tool for Analyzing the Structures of Carbohydrate Sugar Chains.
Aoki, K. F., Yamaguchi, A., Ueda, N., Akutsu, T., Mamitsuka, H., Goto, S. and Kanehisa, M.
Nucleic Acids Research, 32, W267-W272, 2004.
[DOI].


Managing and Analyzing Carbohydrate Data.
Aoki, K. F., Ueda, N., Yamaguchi, A., Akutsu, T., Kanehisa, M. and Mamitsuka, H.
ACM SIGMOD Record, 33 (2), 33-38, 2004.
[DOI, PDF].


A General Probabilistic Framework for Mining Labeled Ordered Trees.
Ueda, N., Aoki, K. F. and Mamitsuka, H.
Proceedings of the Fourth SIAM International Conference on Data Mining (SDM 2004), 357-368, Orlando, FL, USA, April, 2004, SIAM.
[PDF].


Mining Biologically Active Patterns in Metabolic Pathways using Microarray Expression Profiles.
Mamitsuka, H., Okuno, Y. and Yamaguchi, A.
ACM SIGKDD Explorations, 5 (2):113-121, 2003.
[DOI, PDF].


Finding the Maximum Common Subgraph of a Partial k-Tree and a Graph with a Polynomially Bounded Number of Spanning Trees.
Yamaguchi, A. and Mamitsuka, H.
Proceedings of the Fourteenth International Symposium on Algorithm and Computation (ISAAC 2003, Lecture Notes in Computer Science, 2906), 58-67, Kyoto, Japan, December, 2003, Springer-Verlag.
[DOI].


Efficient Tree-Matching Methods for Accurate Carbohydrate Database Queries.
Aoki, K. F., Yamaguchi, A., Okuno, Y., Akutsu, T., Ueda, N., Kanehisa, M. and Mamitsuka, H.
Proceedings of the Fourteenth International Conference on Genome Informatics (GIW 2003, Genome Informatics, 14), 134-143, Yokohama, Japan, December, 2003, Universal Academy Press.
[PubMed].


Efficient Mining from Heterogeneous Data Sets for Predicting Protein-Protein Interactions.
Mamitsuka, H.
Proceedings of the Fourteenth International Workshop on Database and Expert Systems, 32-36, Prague, Czech Republic, September, 2003, IEEE Computer Society Press.
[DOI].


Selective Sampling with a Hierarchical Latent Variable Model.
Mamitsuka, H.
Proceedings of the Fifth International Symposium on Intelligent Data Analysis (IDA 2003, Lecture Notes in Computer Science, 2810), 352-363, Berlin, Germany, August, 2003, Springer-Verlag.
[DOI].


Hierarchical Latent Knowledge Analysis for Co-occurrence Data.
Mamitsuka, H.
Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003), 504-511, Washington DC, USA, August, 2003, AAAI Press.
[Abstract (ICML2003)].


Efficient Unsupervised Mining from Noisy Data Sets: Application to Clustering Co-occurrence Data.
Mamitsuka, H.
Proceedings of the Third SIAM International Conference on Data Mining (SDM 2003), 239-243, San Francisco, CA, USA, May, 2003, SIAM.
[PDF].


Detecting Experimental Noise in Protein-Protein Interactions with Iterative Sampling and Model-based Clustering.
Mamitsuka, H.
Proceedings of the Third IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2003), 385-392, Bethesda, MD, USA, March, 2003, IEEE Computer Society Press.
[DOI].


Empirical Evaluation of Ensemble Feature Subset Selection Methods for Learning from a High-Dimensional Database in Drug Design.
Mamitsuka, H.
Proceedings of the Third IEEE International Symposium on Bioinformatics and Bioengineering (BIBE 2003), 253-257, Bethesda, MD, USA, March, 2003, IEEE Computer Society Press.
[DOI].


Empirical Evaluation of a Dynamic Experiment Design Method for Prediction of MHC Class I-binding Peptides.
Udaka, K., Mamitsuka, H., Nakaseko, Y. and Abe, N.
Journal of Immunology, 169 (10):5744-5753, 2002.
[DOI]


Iteratively Selecting Feature Subsets for Mining from High-Dimensional Databases.
Mamitsuka, H.
Proceedings of the Sixth European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2002, Lecture Notes in Artificial Intelligence, 2431), 361-372, Helsinki, Finland, August, 2002, Springer-Verlag.
[DOI].


Active Ensemble Learning - Applications to Data Mining and Bioinformatics - . (in Japanese)
Mamitsuka, H. and Abe, N.
IEICE Transactions, J85-DII (5):717-724, 2002.
Invited paper.
[Abstract (IEICE), Bibtex].


Prediction of MHC Class I binding Peptides by a Query Learning Algorithm based on Hidden Markov Models.
Udaka, K., Mamitsuka, H., Nakaseko, Y. and Abe, N.
Journal of Biological Physics., 28 (2):183-194, 2002.
[DOI].


Efficient Data Mining by Active Learning.
Mamitsuka, H. and Abe, N.
Progress in Discovery Science, Lecture Notes in Artificial Intelligence, 2281:258-267, 2002, Springer-Verlag.
[DOI].


Unrefereed Publications

Machine Learning for Marketing.
Mamitsuka, H.
Global Data Science Publishing, 2019.
Authored Book.
[Supporting page].


CalCleaveMKL: a Tool for Calpain Cleavage Prediction.
duVerle, D. and Mamitsuka, H.
Calpain: Methods and Protocols Methods in Molecular Biology, 1915, 121-147, 2019.
Book Chapter.
[DOI].


Textbook of Machine Learning and Data Mining (with Bioinformatics Applications).
Mamitsuka, H.
Global Data Science Publishing, 2018.
Authored Book.
[Supporting page].


Data Mining for Systems Biology: Methods and Protocols (2nd Edition).
Mamitsuka, H.
Methods in Molecular Biology, 1807, 2018.
Edited Book.
[DOI].


SiBIC: a Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining.
Takahashi, K., duVerle, D. A., Yotsukura, S., Takigawa, I. and Mamitsuka, H.
Data Mining for Systems Biology: Methods and Protocols. (2nd Edition) Methods in Molecular Biology, 1807, 95-111, 2018.
Book Chapter.
[DOI].


DrugE-Rank: Predicting Drug-target Interactions by Learning to Rank.
Deng, J., Yuan, Q., Mamitsuka, H. and Zhu, S.
Data Mining for Systems Biology: Methods and Protocols. (2nd Edition) Methods in Molecular Biology, 1807, 195-202, 2018.
Book Chapter.
[DOI].


MeSHLabeler and DeepMeSH: Recent Progress in Large-scale MeSH Indexing.
Peng, S., Mamitsuka, H. and Zhu, S.
Data Mining for Systems Biology: Methods and Protocols. (2nd Edition) Methods in Molecular Biology, 1807, 203-209, 2018.
Book Chapter.
[DOI].


MetaMHCpan, A Meta Apporach for Pan-specific MHC Peptide Binding Prediction.
Xu, Y., Luo, C., Mamitsuka, H. and Zhu, S.
Vaccine Design: Methods and Protocols, Volume 2: Vaccines for Veterinary Diseases Methods in Molecular Biology, 1404, 753-760, 2016.
Book Chapter.
[DOI].


An In Silico Model for Interpreting Polypharmacology in Drug-Target Networks.
Takigawa, I., Tsuda, K. and Mamitsuka, H.
In Siilico Models for Drug Discovery Methods in Molecular Biology, 993, 67-80, 2013.
Book Chapter.
[DOI].


Identifying Pathways of Co-ordinated Gene Expression.
Hancock, T., Takigawa, I. and Mamitsuka, H.
Data Mining for Systems Biology: Methods and Protocols Methods in Molecular Biology, 939, 69-85, 2013.
Book Chapter.
[DOI].


Data Mining for Systems Biology: Methods and Protocols.
Mamitsuka, H., DeLisi, C. and Kanehisa, M.
Methods in Molecular Biology, 939, 2013.
Edited Book.
[DOI].


Glycoinformatics: Data Mining-based Approaches.
Mamitsuka, H.
Chimia, 65(1/2), 10-13, 2011.
Invited Review Paper.
[DOI].


Mining Patterns from Glycan Structures.
Takigawa, I., Hashimoto, K., Shiga, M., Kanehisa, M. and Mamitsuka, H.
Proceedings of the International Beilstein Symposium on Glyco-Bioinformatics, 13-24, 2010.
[Beilstein Institute, Bibtex].


On the Performance of Methods for Finding a Switching Mechanism in Gene Expression.
Kayano, M., Takigawa, I., Shiga, M., Tsuda, K. and Mamitsuka, H.
Proceedings of the Tenth Annual International Workshop on Bioinformatics and Systems Biology (IBSB 2010, Genome Informatics, 24), 69-83, Kyoto, Japan, July, 2010, Imperial College Press.
[DOI].


Active Pathway Identification and Classification with Probabilistic Ensembles.
Hancock, T. and Mamitsuka, H.
Proceedings of the Ninth Annual International Workshop on Bioinformatics and Systems Biology (IBSB 2009, Genome Informatics, 22), 30-40, Boston, USA, July, 2009, Imperial College Press.
[DOI].


Annotating Gene Functions with Integrative Spectral Clustering on Microarray Expressions and Sequences.
Li, L., Shiga, M., Ching, W.-K. and Mamitsuka, H.
Proceedings of the Ninth Annual International Workshop on Bioinformatics and Systems Biology (IBSB 2009, Genome Informatics, 22), 95-120, Boston, USA, July, 2009, Imperial College Press.
[DOI].


CaMPDB: a Resource for Calpain and Modulatory Proteolysis.
duVerle, D., Takigawa, I., Ono, Y., Sorimachi, H. and Mamitsuka, H.
Proceedings of the Ninth Annual International Workshop on Bioinformatics and Systems Biology (IBSB 2009, Genome Informatics, 22), 202-214, Boston, USA, July, 2009, Imperial College Press.
[DOI].


Semi-Supervised Graph Partitioning with Decision Trees.
Hancock, T. and Mamitsuka, H.
Proceedings of the Eighth Annual International Workshop on Bioinformatics and Systems Biology (IBSB 2008, Genome Informatics, 20), 102-111, Berlin, Germany, June, 2008, Imperial College Press.
[DOI].


A Framework for Determining Outlying Microarray Experiments.
Wan, R., Wheelock, A. and Mamitsuka, H.
Proceedings of the Eighth Annual International Workshop on Bioinformatics and Systems Biology (IBSB 2008, Genome Informatics, 20), 64-76, Berlin, Germany, June, 2008, Imperial College Press.
[DOI].


Clustering Analysis for Combining Multiple Genomic Data.
Shiga, M., Takigawa, I. and Mamitsuka, H.
Seibutsu-butsuri (Biophysics), 48 (3), 190-194, 2008.
[DOI].


PURE: A PubMed Article Recommendation System Based on Content-based Filtering.
Yoneya, T. and Mamitsuka, H.
Proceedings of the Seventh International Workshop on Bioinformatics and Systems Biology (IBSB 2007, Genome Informatics, 18), 267-276, Tokyo, Japan, July, 2007, Imperial College Press.
[DOI].


Computational Intelligence in Solving Bioinformatics Problems
Cios, K. J., Mamitsuka, H., Nagashima, T. and Tadeusiewicz, R.
Artificial Intelligence in Medicine, 35 (1), 1-8, 2005.
[DOI].


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