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Hagit Shatkay: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Hagit Shatkay, Stanley B. Zdonik
    Approximate Queries and Representations for Large Data Sequences. [Citation Graph (3, 20)][DBLP]
    ICDE, 1996, pp:536-545 [Conf]
  2. Hagit Shatkay, W. John Wilbur
    Finding Themes in Medline Documents: Probabilistic Similarity Search. [Citation Graph (0, 0)][DBLP]
    ADL, 2000, pp:183-192 [Conf]
  3. Nawei Chen, Hagit Shatkay, Dorothea Blostein
    Exploring a new space of features for document classification: figure clustering. [Citation Graph (0, 0)][DBLP]
    CASCON, 2006, pp:369-372 [Conf]
  4. Nawei Chen, Hagit Shatkay, Dorothea Blostein
    Use of Figures in Literature Mining for Biomedical Digital Libraries. [Citation Graph (0, 0)][DBLP]
    DIAL, 2006, pp:180-197 [Conf]
  5. Hagit Shatkay, Leslie Pack Kaelbling
    Heading in the Right Direction. [Citation Graph (0, 0)][DBLP]
    ICML, 1998, pp:531-539 [Conf]
  6. Hagit Shatkay, Leslie Pack Kaelbling
    Learning Topological Maps with Weak Local Odometric Information. [Citation Graph (0, 0)][DBLP]
    IJCAI (2), 1997, pp:920-929 [Conf]
  7. Phil Hyoun Lee, Hagit Shatkay
    BNTagger: improved tagging SNP selection using Bayesian networks. [Citation Graph (0, 0)][DBLP]
    ISMB (Supplement of Bioinformatics), 2006, pp:211-219 [Conf]
  8. Hagit Shatkay, Nawei Chen, Dorothea Blostein
    Integrating image data into biomedical text categorization. [Citation Graph (0, 0)][DBLP]
    ISMB (Supplement of Bioinformatics), 2006, pp:446-453 [Conf]
  9. Hagit Shatkay, Stephen Edwards, W. John Wilbur, Mark Boguski
    Genes, Themes, and Microarrays: Using Information Retrieval for Large-Scale Gene Analysis. [Citation Graph (0, 0)][DBLP]
    ISMB, 2000, pp:317-328 [Conf]
  10. Annette Höglund, Torsten Blum, Scott Brady, Pierre Dönnes, John San Miguel, Matthew Rocheford, Oliver Kohlbacher, Hagit Shatkay
    Significantly Improved Prediction of Subcellular Localization by Integrating Text and Protein Sequence Data . [Citation Graph (0, 0)][DBLP]
    Pacific Symposium on Biocomputing, 2006, pp:16-27 [Conf]
  11. Hagit Shatkay
    Learning Hidden Markov Models with Geometrical Constraints. [Citation Graph (0, 0)][DBLP]
    UAI, 1999, pp:602-611 [Conf]
  12. Hagit Shatkay
    Hairpins in bookstacks: Information retrieval from biomedical text. [Citation Graph (0, 0)][DBLP]
    Briefings in Bioinformatics, 2005, v:6, n:3, pp:222-238 [Journal]
  13. Malik Yousef, Michael Nebozhyn, Hagit Shatkay, Stathis Kanterakis, Louise C. Showe, Michael K. Showe
    Combining multi-species genomic data for microRNA identification using a Naïve Bayes classifier. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2006, v:22, n:11, pp:1325-1334 [Journal]
  14. Monica Chagoyen, Pedro Carmona-Saez, Hagit Shatkay, José María Carazo, Alberto D. Pascual-Montano
    Discovering semantic features in the literature: a foundation for building functional associations. [Citation Graph (0, 0)][DBLP]
    BMC Bioinformatics, 2006, v:7, n:, pp:41- [Journal]
  15. Hagit Shatkay, Stephen Edwards, Mark Boguski
    Information Retrieval Meets Gene Analysis. [Citation Graph (0, 0)][DBLP]
    IEEE Intelligent Systems, 2002, v:17, n:2, pp:45-53 [Journal]
  16. Hagit Shatkay, Leslie Pack Kaelbling
    Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Topological-Geometrical Gap. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 2002, v:16, n:, pp:167-207 [Journal]
  17. Hagit Shatkay, Ronen Feldman
    Mining the Biomedical Literature in the Genomic Era: An Overview. [Citation Graph (0, 0)][DBLP]
    Journal of Computational Biology, 2003, v:10, n:6, pp:821-855 [Journal]
  18. Hagit Shatkay, Jason Miller, Clark M. Mobarry, Michael Flanigan, Shibu Yooseph, Granger G. Sutton
    ThurGood: Evaluating Assembly-to-Assembly Mapping. [Citation Graph (0, 0)][DBLP]
    Journal of Computational Biology, 2004, v:11, n:5, pp:800-811 [Journal]
  19. Phil Hyoun Lee, Hagit Shatkay
    Two Birds, One Stone: Selecting Functionally Informative Tag SNPs for Disease Association Studies. [Citation Graph (0, 0)][DBLP]
    WABI, 2007, pp:61-72 [Conf]
  20. Tiago H. Falk, Hagit Shatkay, Wai-Yip Chan
    Breast Cancer Prognosis via Gaussian Mixture Regression. [Citation Graph (0, 0)][DBLP]
    CCECE, 2006, pp:987-990 [Conf]
  21. Hagit Shatkay, Annette Höglund, Scott Brady, Torsten Blum, Pierre Dönnes, Oliver Kohlbacher
    SherLoc: high-accuracy prediction of protein subcellular localization by integrating text and protein sequence data. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2007, v:23, n:11, pp:1410-1417 [Journal]

  22. EpiLoc: A (Working) Text-Based System for Predicting Protein Subcellular Location. [Citation Graph (, )][DBLP]


  23. Applying Probabilistic Thematic Clustering for Classification in the TREC 2005 Genomics Track. [Citation Graph (, )][DBLP]


  24. Multi-dimensional classification of biomedical text: Toward automated, practical provision of high-utility text to diverse users. [Citation Graph (, )][DBLP]


  25. An integrative scoring system for ranking SNPs by their potential deleterious effects. [Citation Graph (, )][DBLP]


  26. Ranking single nucleotide polymorphisms by potential deleterious effects. [Citation Graph (, )][DBLP]


  27. New directions in biomedical text annotation: definitions, guidelines and corpus construction. [Citation Graph (, )][DBLP]


  28. Functionally informative tag SNP selection using a pareto-optimal approach: playing the game of life. [Citation Graph (, )][DBLP]


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