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Hiroshi Mamitsuka Vis

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

Coauthor Index

1Naoki Abe [2] [3] [7] [9] [10] [11] [12] [42]
2Tatsuya Akutsu [22] [23] [26] [29] [30]
3Vo Ngoc Anh [39] [44] [54]
4Kiyoko F. Aoki-Kinoshita (Kiyoko F. Aoki) [22] [23] [24] [25] [26] [29] [30] [33] [37] [40] [41] [48]
5Krzysztof J. Cios [32]
6Susumu Goto [23]
7Timothy Hancock [53]
8Kosuke Hashimoto [40] [48] [50]
9Minoru Kanehisa [22] [23] [26] [30] [40] [41] [48] [50]
10Tomomasa Nagashima [32]
11Atsuyoshi Nakamura [10]
12Yasushi Okuno [14] [27] [34]
13Alessandro Sette [37]
14Motoki Shiga [45] [46] [50]
15John Sidney [37]
16Ryszard Tadeusiewicz [32]
17Ichigaku Takigawa [38] [39] [45] [46] [47] [49] [50] [51]
18Gozoh Tsujimoto [34]
19Keiko Udaka [37]
20Nobuhisa Ueda [22] [23] [25] [26] [29] [40] [41] [48]
21Raymond Wan [33] [38] [39] [44] [54]
22Atsuko Yamaguchi [14] [16] [22] [23] [24] [26] [29]
23Kenji Yamanishi [1] [5]
24Takashi Yoneya [43]
25Jia Zeng [51] [52]
26Shuqin Zhang [47]
27Shanfeng Zhu [34] [37] [47] [51] [52]

Colors in the list of coauthors

Copyright © Tue Nov 3 08:52:44 2009 by Michael Ley (ley@uni-trier.de)