Hiroshi Mamitsuka

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2012
64Efficient semi-supervised learning on locally informative multiple graphs. Motoki Shiga, Hiroshi Mamitsuka. Pattern Recognition (45): 1035-1049 (2012). Web SearchBibTeXDownload
2011
63Kernels for Link Prediction with Latent Feature Models. Canh Hao Nguyen, Hiroshi Mamitsuka. ECML/PKDD (2) 2011, 517-532. Web SearchBibTeXDownload
62Discriminative Graph Embedding for Label Propagation. Canh Hao Nguyen, Hiroshi Mamitsuka. IEEE Transactions on Neural Networks (22): 1395-1405 (2011). Web SearchBibTeXDownload
61A spectral approach to clustering numerical vectors as nodes in a network. Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka. Pattern Recognition (44): 236-251 (2011). Web SearchBibTeXDownload
60Clustering genes with expression and beyond. Motoki Shiga, Hiroshi Mamitsuka. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery (1): 496-511 (2011). Web SearchBibTeXDownload
2010
59Mining metabolic pathways through gene expression. Timothy Hancock, Ichigaku Takigawa, Hiroshi Mamitsuka. Bioinformatics (26): 2128-2135 (2010). Web SearchBibTeXDownload
58Boosted Optimization for Network Classification. Timothy Hancock, Hiroshi Mamitsuka. Journal of Machine Learning Research - Proceedings Track (9): 305-312 (2010). Web SearchBibTeXDownload
57MetaMHC: a meta approach to predict peptides binding to MHC molecules. Xihao Hu, Wenjian Zhou, Keiko Udaka, Hiroshi Mamitsuka, Shanfeng Zhu. Nucleic Acids Research (38): 474-479 (2010). Web SearchBibTeXDownload
56Algorithms for Finding a Minimum Repetition Representation of a String. Atsuyoshi Nakamura, Tomoya Saito, Ichigaku Takigawa, Hiroshi Mamitsuka, Mineichi Kudo. SPIRE 2010, 185-190. Web SearchBibTeXDownload
2009
55Efficient Probabilistic Latent Semantic Analysis through Parallelization. Raymond Wan, Vo Ngoc Anh, Hiroshi Mamitsuka. AIRS 2009, 432-443. Web SearchBibTeXDownload
54Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data. Mitsunori Kayano, Ichigaku Takigawa, Motoki Shiga, Koji Tsuda, Hiroshi Mamitsuka. Bioinformatics (25): 2735-2743 (2009). Web SearchBibTeXDownload
53Enhancing MEDLINE document clustering by incorporating MeSH semantic similarity. Shanfeng Zhu, Jia Zeng, Hiroshi Mamitsuka. Bioinformatics (25): 1944-1951 (2009). Web SearchBibTeXDownload
52Field independent probabilistic model for clustering multi-field documents. Shanfeng Zhu, Ichigaku Takigawa, Jia Zeng, Hiroshi Mamitsuka. Inf. Process. Manage. (45): 555-570 (2009). Web SearchBibTeXDownload
51A Markov Classification Model for Metabolic Pathways. Timothy Hancock, Hiroshi Mamitsuka. WABI 2009, 121-132. Web SearchBibTeXDownload
2008
50Probabilistic path ranking based on adjacent pairwise coexpression for metabolic transcripts analysis. Ichigaku Takigawa, Hiroshi Mamitsuka. Bioinformatics (24): 250-257 (2008). Web SearchBibTeXDownload
49Mining significant tree patterns in carbohydrate sugar chains. Kosuke Hashimoto, Ichigaku Takigawa, Motoki Shiga, Minoru Kanehisa, Hiroshi Mamitsuka. ECCB 2008, 167-173. Web SearchBibTeXDownload
48A new efficient probabilistic model for mining labeled ordered trees applied to glycobiology. Kosuke Hashimoto, Kiyoko F. Aoki-Kinoshita, Nobuhisa Ueda, Minoru Kanehisa, Hiroshi Mamitsuka. TKDD (2) (2008). Web SearchBibTeXDownload
2007
47A hidden Markov model-based approach for identifying timing differences in gene expression under different experimental factors. Takashi Yoneya, Hiroshi Mamitsuka. Bioinformatics (23): 842-849 (2007). Web SearchBibTeXDownload
46A Probabilistic Model for Clustering Text Documents with Multiple Fields. Shanfeng Zhu, Ichigaku Takigawa, Shuqin Zhang, Hiroshi Mamitsuka. ECIR 2007, 331-342. Web SearchBibTeXDownload
45Annotating gene function by combining expression data with a modular gene network. Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka. ISMB/ECCB (Supplement of Bioinformatics) 2007, 468-478. Web SearchBibTeXDownload
44A spectral clustering approach to optimally combining numericalvectors with a modular network. Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka. KDD 2007, 647-656. Web SearchBibTeXDownload
43Active ensemble learning: Application to data mining and bioinformatics. Hiroshi Mamitsuka, Naoki Abe. Systems and Computers in Japan (38): 100-108 (2007). Web SearchBibTeXDownload
42Passage Retrieval with Vector Space and Query-Level Aspect Models. Raymond Wan, Vo Ngoc Anh, Hiroshi Mamitsuka. TREC 2007. Web SearchBibTeXDownload
2006
41Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules. Shanfeng Zhu, Keiko Udaka, John Sidney, Alessandro Sette, Kiyoko F. Aoki-Kinoshita, Hiroshi Mamitsuka. Bioinformatics (22): 1648-1655 (2006). Web SearchBibTeXDownload
40ProfilePSTMM: capturing tree-structure motifs in carbohydrate sugar chains. Kiyoko F. Aoki-Kinoshita, Nobuhisa Ueda, Hiroshi Mamitsuka, Minoru Kanehisa. ISMB (Supplement of Bioinformatics) 2006, 25-34. Web SearchBibTeXDownload
39A new efficient probabilistic model for mining labeled ordered trees. Kosuke Hashimoto, Kiyoko F. Aoki-Kinoshita, Nobuhisa Ueda, Minoru Kanehisa, Hiroshi Mamitsuka. KDD 2006, 177-186. Web SearchBibTeXDownload
38Query-learning-based iterative feature-subset selection for learning from high-dimensional data sets. Hiroshi Mamitsuka. Knowl. Inf. Syst. (9): 91-108 (2006). Web SearchBibTeXDownload
37Selecting features in microarray classification using ROC curves. Hiroshi Mamitsuka. Pattern Recognition (39): 2393-2404 (2006). Web SearchBibTeXDownload
36Combining Vector-Space and Word-Based Aspect Models for Passage Retrieval. Raymond Wan, Ichigaku Takigawa, Hiroshi Mamitsuka, Vo Ngoc Anh. TREC 2006. Web SearchBibTeXDownload
35Applying Gaussian Distribution-Dependent Criteria to Decision Trees for High-Dimensional Microarray Data. Raymond Wan, Ichigaku Takigawa, Hiroshi Mamitsuka. VDMB 2006, 40-49. Web SearchBibTeXDownload
2005
34Computational intelligence in solving bioinformatics problems. Krzysztof J. Cios, Hiroshi Mamitsuka, Tomomasa Nagashima, Ryszard Tadeusiewicz. Artificial Intelligence in Medicine (35): 1-8 (2005). Web SearchBibTeXDownload
33Finding the biologically optimal alignment of multiple sequences. Hiroshi Mamitsuka. Artificial Intelligence in Medicine (35): 9-18 (2005). Web SearchBibTeXDownload
32A score matrix to reveal the hidden links in glycans. Kiyoko F. Aoki, Hiroshi Mamitsuka, Tatsuya Akutsu, Minoru Kanehisa. Bioinformatics (21): 1457-1463 (2005). Web SearchBibTeXDownload
31A probabilistic model for mining implicit 'chemical compound-gene' relations from literature. Shanfeng Zhu, Yasushi Okuno, Gozoh Tsujimoto, Hiroshi Mamitsuka. ECCB/JBI 2005, 251. Web SearchBibTeXDownload
30Essential Latent Knowledge for Protein-Protein Interactions: Analysis by an Unsupervised Learning Approach. Hiroshi Mamitsuka. IEEE/ACM Trans. Comput. Biology Bioinform. (2): 119-130 (2005). Web SearchBibTeXDownload
29A Probabilistic Model for Mining Labeled Ordered Trees: Capturing Patterns in Carbohydrate Sugar Chains. Nobuhisa Ueda, Kiyoko F. Aoki-Kinoshita, Atsuko Yamaguchi, Tatsuya Akutsu, Hiroshi Mamitsuka. IEEE Trans. Knowl. Data Eng. (17): 1051-1064 (2005). Web SearchBibTeXDownload
28Cleaning microarray expression data using Markov random fields based on profile similarity. Raymond Wan, Hiroshi Mamitsuka, Kiyoko F. Aoki. SAC 2005, 206-207. Web SearchBibTeXDownload
2004
27A Hierarchical Mixture of Markov Models for Finding Biologically Active Metabolic Paths Using Gene Expression and Protein Classes. Hiroshi Mamitsuka, Yasushi Okuno. CSB 2004, 341-352. Web SearchBibTeXDownload
26Finding the maximum common subgraph of a partial k-tree and a graph with a polynomially bounded number of spanning trees. Atsuko Yamaguchi, Kiyoko F. Aoki, Hiroshi Mamitsuka. Inf. Process. Lett. (92): 57-63 (2004). Web SearchBibTeXDownload
25Application of a new probabilistic model for recognizing complex patterns in glycans. Kiyoko F. Aoki, Nobuhisa Ueda, Atsuko Yamaguchi, Minoru Kanehisa, Tatsuya Akutsu, Hiroshi Mamitsuka. ISMB/ECCB (Supplement of Bioinformatics) 2004, 6-14. Web SearchBibTeXDownload
24KCaM (KEGG Carbohydrate Matcher): a software tool for analyzing the structures of carbohydrate sugar chains. Kiyoko F. Aoki, Atsuko Yamaguchi, Nobuhisa Ueda, Tatsuya Akutsu, Hiroshi Mamitsuka, Susumu Goto, Minoru Kanehisa. Nucleic Acids Research (32): 267-272 (2004). Web SearchBibTeX
23A General Probabilistic Framework for Mining Labeled Ordered Trees. Nobuhisa Ueda, Kiyoko F. Aoki, Hiroshi Mamitsuka. SDM 2004. Web SearchBibTeXDownload
22Managing and Analyzing Carbohydrate Data. Kiyoko F. Aoki, Nobuhisa Ueda, Atsuko Yamaguchi, Tatsuya Akutsu, Minoru Kanehisa, Hiroshi Mamitsuka. SIGMOD Record (33): 33-38 (2004). Web SearchBibTeXDownload
2003
21Empirical Evaluation of Ensemble Feature Subset Selection Methods for Learning from a High-Dimensional Database in Drug Desig. Hiroshi Mamitsuka. BIBE 2003, 253-257. Web SearchBibTeXDownload
20Detecting Experimental Noises in Protein-Protein Interactions with Iterative Sampling and Model-Based Clustering. Hiroshi Mamitsuka. BIBE 2003, 385-392. Web SearchBibTeXDownload
19Efficient Mining from Heterogeneous Data Sets for Predicting Protein-Protein Interactions. Hiroshi Mamitsuka. DEXA Workshops 2003, 32-36. Web SearchBibTeXDownload
18Hierarchical Latent Knowledge Analysis for Co-occurrence Data. Hiroshi Mamitsuka. ICML 2003, 504-511. Web SearchBibTeX
17Selective Sampling with a Hierarchical Latent Variable Model. Hiroshi Mamitsuka. IDA 2003, 352-363. Web SearchBibTeXDownload
16Finding the Maximum Common Subgraph of a Partial k-Tree and a Graph with a Polynomially Bounded Number of Spanning Trees. Atsuko Yamaguchi, Hiroshi Mamitsuka. ISAAC 2003, 58-67. Web SearchBibTeXDownload
15Efficient Unsupervised Mining from Noisy Data Sets: Application to Clustering Co-occurrence Data. Hiroshi Mamitsuka. SDM 2003. Web SearchBibTeXDownload
14Mining biologically active patterns in metabolic pathways using microarray expression profiles. Hiroshi Mamitsuka, Yasushi Okuno, Atsuko Yamaguchi. SIGKDD Explorations (5): 113-121 (2003). Web SearchBibTeXDownload
2002
13Iteratively Selecting Feature Subsets for Mining from High-Dimensional Databases. Hiroshi Mamitsuka. PKDD 2002, 361-372. Web SearchBibTeXDownload
12Efficient Data Mining by Active Learning. Hiroshi Mamitsuka, Naoki Abe. Progress in Discovery Science 2002, 258-267. Web SearchBibTeXDownload
2000
11Efficient Mining from Large Databases by Query Learning. Hiroshi Mamitsuka, Naoki Abe. ICML 2000, 575-582. Web SearchBibTeX
1998
10Empirical Comparison of Competing Query Learning Methods. Naoki Abe, Hiroshi Mamitsuka, Atsuyoshi Nakamura. Discovery Science 1998, 387-388. Web SearchBibTeXDownload
9Query Learning Strategies Using Boosting and Bagging. Naoki Abe, Hiroshi Mamitsuka. ICML 1998, 1-9. Web SearchBibTeX
1997
8Predicting Protein Secondary Structure Using Stochastic Tree Grammars. Naoki Abe, Hiroshi Mamitsuka. Machine Learning (29): 275-301 (1997). Web SearchBibTeXDownload
7Supervised learning of hidden Markov models for sequence discrimination. Hiroshi Mamitsuka. RECOMB 1997, 202-208. Web SearchBibTeXDownload
1996
6A Learning Method of Hidden Markov Models for Sequence Discrimination. Hiroshi Mamitsuka. Journal of Computational Biology (3): 361-374 (1996). Web SearchBibTeX
1995
5alpha-Helix region prediction with stochastic rule learning. Hiroshi Mamitsuka, Kenji Yamanishi. Computer Applications in the Biosciences (11): 399-411 (1995). Cited by 2Web SearchBibTeXDownload
4Representing inter-residue dependencies in protein sequences with probabilistic networks. Hiroshi Mamitsuka. Computer Applications in the Biosciences (11): 413-422 (1995). Web SearchBibTeXDownload
1994
3A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars. Naoki Abe, Hiroshi Mamitsuka. ICML 1994, 3-11. Web SearchBibTeX
2Predicting Location and Structure Of beta-Sheet Regions Using Stochastic Tree Grammars. Hiroshi Mamitsuka, Naoki Abe. ISMB 1994, 276-284. Web SearchBibTeX
1992
1Protein Secondary Structure Prediction Based on Stochastic-Rule Learning. Hiroshi Mamitsuka, Kenji Yamanishi. ALT 1992, 240-251. Cited by 5Web SearchBibTeXDownload
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