Identifying relevant data for a biological database: Handcrafted rules versus machine learning

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 8, Issue 3, 2011, Pages 851-857

  Sehgal, Aditya Kumar ^a   Das, Sanmay ^b   Noto, Keith ^c   Saier Jr , Milton H ^d   Elkan, Charles ^c  

^a Parity Computing Inc   (United States)

^b Rensselaer Polytechnic Institute   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

Author keywords

association rules; Bioinformatics (genome or protein) databases; biomedical text classification; classification; clustering; data mining; text mining

Indexed keywords

BIOINFORMATICS (GENOME OR PROTEIN) DATABASES; BIOMEDICAL TEXT; CLASSIFICATION; CLUSTERING; TEXT MINING;

ASSOCIATION RULES; BIOINFORMATICS; CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; DATA MINING; GENES; LEARNING SYSTEMS; NATURAL LANGUAGE PROCESSING SYSTEMS; PROTEINS; TEXT PROCESSING;

DATABASE SYSTEMS;

CARRIER PROTEIN; PROTEIN;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; CLASSIFICATION; CLUSTER ANALYSIS; COMPARATIVE STUDY; DATA MINING; GENETIC DATABASE; GENETICS; GENOMICS; HUMAN; MEDLINE; METHODOLOGY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CARRIER PROTEINS; CLUSTER ANALYSIS; DATA MINING; DATABASES, GENETIC; GENOMICS; HUMANS; MEDLINE; PROTEINS;

EID: 79952843320     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2009.83     Document Type: Article

References (37)

1. J. A. Aslam, V. Pavlu, and E. Yilmaz, "A Statistical Method for System Evaluation Using Incomplete Judgments", Proc. ACM SIGIR, pp. 541-548, 2006.
2. E. Camon et al., "The Gene Ontology Annotation (GOA) Project: Implementation of GO in Swiss-Prot, TrEMBL, and InterPro", Genome Research, vol. 13, no. 4, pp. 662-672, 2003. (Pubitemid 36511925)
3. Y. Chen et al., "SPD-A Web-Based Secreted Protein Database", Nucleic Acids Research, vol. 33, pp. D169-D173, 2005. (Pubitemid 40207853)
4. F. M. Couto, B. Martins, and M. J. Silva, "Classifying Biological Articles Using Web Resources", Proc. ACM Symp. Applied Computing, pp. 111-115, 2004.
5. M. Craven and J. Kumlien, "Constructing Biological Knowledge Bases by Extracting Information from Text Sources", Proc. Seventh Int'l. Conf. Intelligent Systems for Molecular Biology, 1999.
6. S. Das, M. H. Saier, Jr., and C. Elkan, "Finding Transport Proteins in a General Protein Database", Proc. 11th European Conf. Principles and Practice of Knowledge Discovery in Databases, pp. 54-66, 2007.
7. G. Dellaire, R. Farrall, and W. A. Bickmore, "The Nuclear Protein Database (NPD): Sub-Nuclear Localisation and Functional Annotation of the Nuclear Proteome", Nucleic Acids Research, vol. 31, pp. 328-330, 2003. (Pubitemid 36157283)
8. F. Denis, "PAC Learning from Positive Statistical Queries", Proc. Ninth Int'l Conf. Algorithmic Learning Theory (ALT'98), pp 112-126, 1998. (Pubitemid 128149820)
9. F. Denis, R. Gilleron, and M. Tommasi, "Text Classification from Positive and Unlabeled Examples", Proc. Conf. Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU'02), pp. 1927-1934, 2002.
10. I. Donaldson et al., "PreBIND and Textomy-Mining the Biomedical Literature for Protein-Protein Interactions Using a Support Vector Machine", BMC Bioinformatics, vol. 4, no. 11, Mar. 2003.
11. C. Elkan and K. Noto, "Learning Classifiers from Only Positive and Unlabeled Data", Proc. 14th ACM SIGKDD (KDD'08), pp. 213-220, 2008.
12. G. P. C. Fung, J. X. Yu, H. Lu, and P. S. Yu, "Text Classification without Negative Examples Revisit (sic)", IEEE Trans. Knowledge and Data Eng., vol. 18, no. 1, pp. 6-20, Jan. 2006. (Pubitemid 43145085)
13. M. Y. Galperin and G. R. Cochrane, "Nucleic Acids Research Annual Database Issue and the NAR Online Molecular Biology Database Collection in 2009", Nucleic Acids Research, vol. 37, pp. D1-D4, 2009.
14. M. Ashburner et al., "Gene Ontology: Tool for the Unification of Biology", Nature Genetics, vol. 25, pp. 25-29, May 2000. (Pubitemid 30257031)
15. G. Grumbling and V. Strelets, "The FlyBase Consortium. Flybase: Anatomical Data, Images and Queries", Nucleic Acids Research, vol. 34, pp. D484-D488, 2006.
16. W. Hersh, "Evaluation of Biomedical Text-Mining Systems: Lessons Learned from Information Retrieval", Briefings in Bioinformatics, vol. 6, no. 4, pp. 344-356, Dec. 2005. (Pubitemid 43091940)
17. W. Hersh, A. Cohen, J. Yang, R. T. Bhupatiraju, P. Roberts, and M. Hearst, "Trec 2005 Genomics Track Overview", Proc. Text REtrieval Conf. (TREC), 2005.
18. W. Hou, C. Lee, and H. Chen, "Classifying Biological Full-Text Articles for Multi-Database Curation", Proc. European Chapter of Assoc. for Computational Linguistics (ACL), pp. 159-162, Apr. 2006.
19. M. Iwayama, A. Fujii, N. Kando, and Y. Marukawa, "An Empirical Study on Retrieval Models for Different Document Genres: Patents and Newspaper Articles", Proc. ACM SIGIR, pp. 251-258, 2003.
20. T. Joachims, "Making Large-Scale Support Vector Machine Learning Practical", Advances in Kernel Methods: Support Vector Machines, B. Schölkopf, C. Burges, and A. Smola, eds., pp. 169-184, MIT Press, 1998.
21. T. Joachims, "Transductive Inference for Text Classification Using Support Vector Machines", Proc. 16th Int'l Conf. Machine Learning, 1999.
22. M. Krallinger and A. Valencia, "Text-Mining and Information- Retrieval Services for Molecular Biology", Genome Biology, vol. 6, no. 7, pp. 224-230, 2005.
23. E. Kretschmann, W. Fleischmann, and R. Apweiler, "Automatic Rule Generation for Protein Annotation with the C4.5 Data Mining Algorithm Applied on SWISS-PROT", Bioinformatics, vol. 17, no. 10, pp. 920-926, 2001. (Pubitemid 32998628)
24. B. Liu, Y. Dai, X. Li, W. S. Lee, and P. S. Yu, "Building Text Classifiers Using Positive and Unlabeled Examples", Proc. Third IEEE Int'l Conf. Data Mining (ICDM'03), pp. 179-188, 2003.
25. H. Liu, M. Torii, G. Xu, Z. Hu, and J. Goll, "Learning from Positive and Unlabeled Documents for Retrieval of Bacterial Protein-Protein Interaction Literature", Lecture Notes in Computer Science, vol. 6004, pp. 62-70, 2010, doi: 10.1007/978-3-642-13131-8.
26. A. K. McCallum, "Bow: A Toolkit for Statistical Language Modeling, Text Retrieval, Classification and Clustering", Unpublished, 1996.
27. A. K. McCallum., "MALLET: A Machine Learning for Language Toolkit", http://mallet.cs.umass.edu, 2002.
28. Y. Regev, M. Finkelstein-Landau, R. Feldman, R. Gorodetsky, X. Zheng, S. Levy, R. Charlab, C. Lawrence, R. A. Lippert, Q. Zhang, and H. Shatkay, "Rule-Based Extraction of Experimental Evidence in the Biomedical Domain-the KDD Cup (Task 1)", ACM SIGKDD Explorations Newsletter, vol. 4, no. 2, pp. 90-92, 2002.
29. M. H. Saier, Jr., C. V. Tran, and R. D. Barabote, "TCDB: The Transporter Classification Database for Membrane Transport Protein Analyses and Information", Nucleic Acids Research, vol. 36, pp. D181-D186, 2006.
30. G. Salton, The SMART Retrieval System; Experiments in Automatic Document Processing. Prentice-Hall, Inc., 1971.
31. G. Salton and C. Buckley, "Term-Weighting Approaches in Automatic Text Retrieval", Information and Process Management, vol. 24, no. 5, pp. 513-523, 1988.
32. B. Schölkopf, J. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson, "Estimating the Support of a High-Dimensional Distribution", Neural Computation, vol. 13, pp. 1443-1471, 2001. (Pubitemid 33595028)
33. B. Schölkopf, A. Smola, R. Williamson, and P. L. Bartlett, "New Support Vector Algorithms", Neural Computation, vol. 12, pp. 1207-1245, 2000.
34. F. Sebastiani, "Machine Learning in Automated Text Categorization", ACM Computing Surveys, vol. 34, pp. 1-47, 2002.
35. H. Shatkay, N. Chen, and D. Blostein, "Integrating Image Data into Biomedical Text Categorization", Bioinformatics, vol. 22, no. 14, pp. e446-e453, 2006. (Pubitemid 44288315)
36. A. S. Yeh, L. Hirschman, and A. A. Morgan, "Evaluation of Text Data Mining for Database Curation: Lessons Learned from the KDD Challenge Cup", Bioinformatics, vol. 19, suppl. 1, pp. i331-i339, 2003.
37. D. Zhang and W. S. Lee, "A Simple Probabilistic Approach to Learning from Positive and Unlabeled Examples", Proc. Fifth Ann. UK Workshop Computational Intelligence (UKCI), pp. 83-87, Sept. 2005.