Substring selection for biomedical document classification

Bioinformatics

Volumn 22, Issue 17, 2006, Pages 2136-2142

  Han, Bo ^a   Obradovic, Zoran ^a   Hu, Zhang Zhi ^b   Wu, Cathy H ^b   Vucetic, Slobodan ^a  

^a TEMPLE UNIVERSITY   (United States)

^b GEORGETOWN UNIVERSITY MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ALGORITHM; AREA UNDER THE CURVE; ARTICLE; INFORMATION RETRIEVAL; MACHINE; PRIORITY JOURNAL; PROTEIN DATABASE; PROTEIN PROCESSING; RECEIVER OPERATING CHARACTERISTIC; ARTIFICIAL INTELLIGENCE; DATA BASE; DOCUMENTATION; LINGUISTICS; MEDLINE; METHODOLOGY; NATURAL LANGUAGE PROCESSING; PUBLICATION;

ABSTRACTING AND INDEXING; ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATABASE MANAGEMENT SYSTEMS; DOCUMENTATION; INFORMATION STORAGE AND RETRIEVAL; MEDLINE; NATURAL LANGUAGE PROCESSING; PERIODICALS; VOCABULARY, CONTROLLED;

EID: 33748635165     PISSN: 13674803     EISSN: 13674811     Source Type: Journal    
DOI: 10.1093/bioinformatics/btl350     Document Type: Article

References (22)

1. Andrade,M.A. et al. (1998) Automatic extraction of keywords from scientific text: Application to the knowledge domain of protein families. Bioinformatics, 14, 600-607.
2. Aphinyanaphongs,Y. and Aliferis,C.F. (2005) Text categorization models for retrieval of high quality articles in internal medicine. J. Am. Med. Inform. Assoc., 12, 207-216.
3. Berry,M.W. et al. (1995) Using linear algebra for intelligent information retrieval. SIAM Rev., 37, 573-595.
4. Cody,W.J. (1969) Rational Chebyshev approximations for the error function. Math. Comp., 22, 631-638.
5. Dobrokhotov,P.B. et al. (2003) Combining NLP and probabilistic categorization for document and term selection for Swiss-Prot medical annotation. Bioinformatics, 19 (Suppl. 1), i91-i94.
6. Ghanem,M.M. et al. (2003) Automatic scientific text classification using local patterns: KDD Cup 2002 (task 1). SIGKDD Explor. Newslett., 4, 95-96.
7. Hu,Z.Z. et al. (2004) iProLINK: An integrated protein resource for literature mining. Comput. Biol. Chem., 28, 409-416.
8. Hu,Z.Z. et al. (2005) Literature mining and database annotation of protein phosphorylation using a rule-based system. Bioinformatics, 21, 2759-2765.
9. Joachims,T. (1998) Text categorization with support vector machines: learning with many relevant features. In Proceedings of the 10th European Conference on Machine Learning, Chemnitz, Germany, pp. 137-142.
10. Joachims,T. (1999) Making large-scale SVM learning practical. In Advances in Kernel Methods - Support Vector Learning. In Schölkopf,B., Burges,C. and Smola,A. (eds), MIT Press, Cambridge, MA, pp. 41-54.
11. Marcotte,E.M. et al. (2001) Mining literature for protein-protein interactions. Bioinformatics, 17, 359-363.
12. McCallum,A. and Nigam,K. (1998) A comparison of event models for Naïve Bayes text classification. In Proceedings of the AAAI-98 Workshop on Learning for Text Categorization, Madison, WI, pp. 41-48.
13. Nenadic,G. et al. (2003) Selecting text features for gene name classification: From documents to terms. In Proceedings of ACL 2003 Workshop on Natural Language Processing in Biomedicine, Sapporo, Japan, pp. 121-128.
14. Porter,M. (1980) An algorithm for sux stripping. Program, 14, 130-137.
15. Regev,Y. et al. (2003) Rulebased extraction of experimental evidence in the biomedical domain - the KDD Cup 2002 (task 1). SIGKDD Explor. Newslett., 4, 90-92.
16. Rice,S.B. et al. (2005) Mining protein function from text using term-based support vector machines. BMC Bioinformatics, 6 (Suppl. 1), S22.
17. Shi,M. et al. (2003) A machine learning approach for the curation of biomedical literature-KDD Cup 2002 (task 1). SIGKDD Explor. Newslett., 4, 93-94.
18. Vapnik,V.N. (1995) The Nature of Statistical Learning Theory. Springer, New York.
19. Wilbur,J.W. (2000) Boosting Naïve Bayesian learning on a large subset of MEDLINE. In Proceedings of AMIA Symposium, Los Angeles, CA, pp. 918-922.
20. Wu,C.H. et al. (2003) The Protein information resource. Nucleic Acids Res., 31, 345-347.
21. Wu,C.H. et al. (2006) The Universal Protein Resource (UniProt): An expanding universe of protein information. Nucleic Acids Res., 34, D187-D191.
22. Yang,Y. and Pederson,J.O. (1997) A comparative study on feature selection in text categorization. In Proceedings of the 14th International Conference on Machine Learning, Nashville, TN, pp. 412-420.