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Hiroshi Mamitsuka: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Hiroshi Mamitsuka, Kenji Yamanishi
    Protein Secondary Structure Prediction Based on Stochastic-Rule Learning. [Citation Graph (0, 0)][DBLP]
    ALT, 1992, pp:240-251 [Conf]
  2. Hiroshi Mamitsuka
    Empirical Evaluation of Ensemble Feature Subset Selection Methods for Learning from a High-Dimensional Database in Drug Desig. [Citation Graph (0, 0)][DBLP]
    BIBE, 2003, pp:253-257 [Conf]
  3. Hiroshi Mamitsuka
    Detecting Experimental Noises in Protein-Protein Interactions with Iterative Sampling and Model-Based Clustering. [Citation Graph (0, 0)][DBLP]
    BIBE, 2003, pp:385-392 [Conf]
  4. Hiroshi Mamitsuka, Yasushi Okuno
    A Hierarchical Mixture of Markov Models for Finding Biologically Active Metabolic Paths Using Gene Expression and Protein Classes. [Citation Graph (0, 0)][DBLP]
    CSB, 2004, pp:341-352 [Conf]
  5. Hiroshi Mamitsuka
    Efficient Mining from Heterogeneous Data Sets for Predicting Protein-Protein Interactions. [Citation Graph (0, 0)][DBLP]
    DEXA Workshops, 2003, pp:32-36 [Conf]
  6. Naoki Abe, Hiroshi Mamitsuka, Atsuyoshi Nakamura
    Empirical Comparison of Competing Query Learning Methods. [Citation Graph (0, 0)][DBLP]
    Discovery Science, 1998, pp:387-388 [Conf]
  7. Hiroshi Mamitsuka, Naoki Abe
    Efficient Data Mining by Active Learning. [Citation Graph (0, 0)][DBLP]
    Progress in Discovery Science, 2002, pp:258-267 [Conf]
  8. Shanfeng Zhu, Yasushi Okuno, Gozoh Tsujimoto, Hiroshi Mamitsuka
    A probabilistic model for mining implicit 'chemical compound-gene' relations from literature. [Citation Graph (0, 0)][DBLP]
    ECCB/JBI, 2005, pp:251- [Conf]
  9. Shanfeng Zhu, Ichigaku Takigawa, Shuqin Zhang, Hiroshi Mamitsuka
    A Probabilistic Model for Clustering Text Documents with Multiple Fields. [Citation Graph (0, 0)][DBLP]
    ECIR, 2007, pp:331-342 [Conf]
  10. Naoki Abe, Hiroshi Mamitsuka
    A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars. [Citation Graph (0, 0)][DBLP]
    ICML, 1994, pp:3-11 [Conf]
  11. Naoki Abe, Hiroshi Mamitsuka
    Query Learning Strategies Using Boosting and Bagging. [Citation Graph (0, 0)][DBLP]
    ICML, 1998, pp:1-9 [Conf]
  12. Hiroshi Mamitsuka, Naoki Abe
    Efficient Mining from Large Databases by Query Learning. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:575-582 [Conf]
  13. Hiroshi Mamitsuka
    Hierarchical Latent Knowledge Analysis for Co-occurrence Data. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:504-511 [Conf]
  14. Hiroshi Mamitsuka
    Selective Sampling with a Hierarchical Latent Variable Model. [Citation Graph (0, 0)][DBLP]
    IDA, 2003, pp:352-363 [Conf]
  15. Atsuko Yamaguchi, Hiroshi Mamitsuka
    Finding the Maximum Common Subgraph of a Partial k-Tree and a Graph with a Polynomially Bounded Number of Spanning Trees. [Citation Graph (0, 0)][DBLP]
    ISAAC, 2003, pp:58-67 [Conf]
  16. Kiyoko F. Aoki-Kinoshita, Nobuhisa Ueda, Hiroshi Mamitsuka, Minoru Kanehisa
    ProfilePSTMM: capturing tree-structure motifs in carbohydrate sugar chains. [Citation Graph (0, 0)][DBLP]
    ISMB (Supplement of Bioinformatics), 2006, pp:25-34 [Conf]
  17. Kiyoko F. Aoki, Nobuhisa Ueda, Atsuko Yamaguchi, Minoru Kanehisa, Tatsuya Akutsu, Hiroshi Mamitsuka
    Application of a new probabilistic model for recognizing complex patterns in glycans. [Citation Graph (0, 0)][DBLP]
    ISMB/ECCB (Supplement of Bioinformatics), 2004, pp:6-14 [Conf]
  18. Hiroshi Mamitsuka, Naoki Abe
    Predicting Location and Structure Of beta-Sheet Regions Using Stochastic Tree Grammars. [Citation Graph (0, 0)][DBLP]
    ISMB, 1994, pp:276-284 [Conf]
  19. Kosuke Hashimoto, Kiyoko F. Aoki-Kinoshita, Nobuhisa Ueda, Minoru Kanehisa, Hiroshi Mamitsuka
    A new efficient probabilistic model for mining labeled ordered trees. [Citation Graph (0, 0)][DBLP]
    KDD, 2006, pp:177-186 [Conf]
  20. Hiroshi Mamitsuka
    Iteratively Selecting Feature Subsets for Mining from High-Dimensional Databases. [Citation Graph (0, 0)][DBLP]
    PKDD, 2002, pp:361-372 [Conf]
  21. Hiroshi Mamitsuka
    Supervised learning of hidden Markov models for sequence discrimination. [Citation Graph (0, 0)][DBLP]
    RECOMB, 1997, pp:202-208 [Conf]
  22. Raymond Wan, Hiroshi Mamitsuka, Kiyoko F. Aoki
    Cleaning microarray expression data using Markov random fields based on profile similarity. [Citation Graph (0, 0)][DBLP]
    SAC, 2005, pp:206-207 [Conf]
  23. Nobuhisa Ueda, Kiyoko F. Aoki, Hiroshi Mamitsuka
    A General Probabilistic Framework for Mining Labeled Ordered Trees. [Citation Graph (0, 0)][DBLP]
    SDM, 2004, pp:- [Conf]
  24. Hiroshi Mamitsuka
    Efficient Unsupervised Mining from Noisy Data Sets: Application to Clustering Co-occurrence Data. [Citation Graph (0, 0)][DBLP]
    SDM, 2003, pp:- [Conf]
  25. Raymond Wan, Ichigaku Takigawa, Hiroshi Mamitsuka
    Applying Gaussian Distribution-Dependent Criteria to Decision Trees for High-Dimensional Microarray Data. [Citation Graph (0, 0)][DBLP]
    VDMB, 2006, pp:40-49 [Conf]
  26. Krzysztof J. Cios, Hiroshi Mamitsuka, Tomomasa Nagashima, Ryszard Tadeusiewicz
    Computational intelligence in solving bioinformatics problems. [Citation Graph (0, 0)][DBLP]
    Artificial Intelligence in Medicine, 2005, v:35, n:1-2, pp:1-8 [Journal]
  27. Hiroshi Mamitsuka
    Finding the biologically optimal alignment of multiple sequences. [Citation Graph (0, 0)][DBLP]
    Artificial Intelligence in Medicine, 2005, v:35, n:1-2, pp:9-18 [Journal]
  28. Kiyoko F. Aoki, Hiroshi Mamitsuka, Tatsuya Akutsu, Minoru Kanehisa
    A score matrix to reveal the hidden links in glycans. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2005, v:21, n:8, pp:1457-1463 [Journal]
  29. Hiroshi Mamitsuka
    Representing inter-residue dependencies in protein sequences with probabilistic networks. [Citation Graph (0, 0)][DBLP]
    Computer Applications in the Biosciences, 1995, v:11, n:4, pp:413-422 [Journal]
  30. Hiroshi Mamitsuka, Kenji Yamanishi
    alpha-Helix region prediction with stochastic rule learning. [Citation Graph (0, 0)][DBLP]
    Computer Applications in the Biosciences, 1995, v:11, n:4, pp:399-411 [Journal]
  31. Shanfeng 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. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2006, v:22, n:13, pp:1648-1655 [Journal]
  32. Takashi Yoneya, Hiroshi Mamitsuka
    A hidden Markov model-based approach for identifying timing differences in gene expression under different experimental factors. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2007, v:23, n:7, pp:842-849 [Journal]
  33. Atsuko 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. [Citation Graph (0, 0)][DBLP]
    Inf. Process. Lett., 2004, v:92, n:2, pp:57-63 [Journal]
  34. Hiroshi Mamitsuka
    A Learning Method of Hidden Markov Models for Sequence Discrimination. [Citation Graph (0, 0)][DBLP]
    Journal of Computational Biology, 1996, v:3, n:3, pp:361-374 [Journal]
  35. Hiroshi Mamitsuka
    Query-learning-based iterative feature-subset selection for learning from high-dimensional data sets. [Citation Graph (0, 0)][DBLP]
    Knowl. Inf. Syst., 2006, v:9, n:1, pp:91-108 [Journal]
  36. Naoki Abe, Hiroshi Mamitsuka
    Predicting Protein Secondary Structure Using Stochastic Tree Grammars. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1997, v:29, n:2-3, pp:275-301 [Journal]
  37. 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. [Citation Graph (0, 0)][DBLP]
    Nucleic Acids Research, 2004, v:32, n:Web-Server-Issue, pp:267-272 [Journal]
  38. Hiroshi Mamitsuka
    Selecting features in microarray classification using ROC curves. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition, 2006, v:39, n:12, pp:2393-2404 [Journal]
  39. Hiroshi Mamitsuka, Yasushi Okuno, Atsuko Yamaguchi
    Mining biologically active patterns in metabolic pathways using microarray expression profiles. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2003, v:5, n:2, pp:113-121 [Journal]
  40. Kiyoko F. Aoki, Nobuhisa Ueda, Atsuko Yamaguchi, Tatsuya Akutsu, Minoru Kanehisa, Hiroshi Mamitsuka
    Managing and Analyzing Carbohydrate Data. [Citation Graph (0, 0)][DBLP]
    SIGMOD Record, 2004, v:33, n:2, pp:33-38 [Journal]
  41. Hiroshi Mamitsuka
    Essential Latent Knowledge for Protein-Protein Interactions: Analysis by an Unsupervised Learning Approach. [Citation Graph (0, 0)][DBLP]
    IEEE/ACM Trans. Comput. Biology Bioinform., 2005, v:2, n:2, pp:119-130 [Journal]
  42. Nobuhisa 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. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Knowl. Data Eng., 2005, v:17, n:8, pp:1051-1064 [Journal]
  43. Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka
    A spectral clustering approach to optimally combining numericalvectors with a modular network. [Citation Graph (0, 0)][DBLP]
    KDD, 2007, pp:647-656 [Conf]

  44. Efficient Probabilistic Latent Semantic Analysis through Parallelization. [Citation Graph (, )][DBLP]

  45. Mining significant tree patterns in carbohydrate sugar chains. [Citation Graph (, )][DBLP]

  46. Annotating gene function by combining expression data with a modular gene network. [Citation Graph (, )][DBLP]

  47. Passage Retrieval with Vector Space and Query-Level Aspect Models. [Citation Graph (, )][DBLP]

  48. Combining Vector-Space and Word-Based Aspect Models for Passage Retrieval. [Citation Graph (, )][DBLP]

  49. A Markov Classification Model for Metabolic Pathways. [Citation Graph (, )][DBLP]

  50. Probabilistic path ranking based on adjacent pairwise coexpression for metabolic transcripts analysis. [Citation Graph (, )][DBLP]

  51. Enhancing MEDLINE document clustering by incorporating MeSH semantic similarity. [Citation Graph (, )][DBLP]

  52. Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data. [Citation Graph (, )][DBLP]

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