Contextual weighting for Support Vector Machines in literature mining: An application to gene versus protein name disambiguation

BMC Bioinformatics

Volumn 6, Issue , 2005, Pages

  Pahikkala, Tapio ^a   Ginter, Filip ^a   Boberg, Jorma ^a   Järvinen, Jouni ^a   Salakoski, Tapio ^a  

^a UNIVERSITY OF TURKU   (Finland)

Author keywords

[No Author keywords available]

Indexed keywords

AREA UNDER ROC CURVE (AUC); CLASSIFICATION PERFORMANCE; DATA MINING TASKS; LITERATURE MINING; NAIVE BAYES CLASSIFIERS; PERCENTAGE POINTS; SUPPORT VECTOR MACHINE (SVMS); WEIGHTING SCHEME;

GENES; PROTEINS;

SUPPORT VECTOR MACHINES;

ARTICLE; CLASSIFICATION; CONTROLLED STUDY; GENOMICS; MATHEMATICAL COMPUTING; PROBABILITY; PROTEOMICS; RECEIVER OPERATING CHARACTERISTIC; STATISTICAL ANALYSIS; STATISTICAL SIGNIFICANCE; VALIDATION PROCESS; ALGORITHM; AREA UNDER THE CURVE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; BIBLIOGRAPHIC DATABASE; BIOLOGY; CHEMISTRY; CLUSTER ANALYSIS; COMPUTER ANALYSIS; COMPUTER PROGRAM; HUMAN; HUMAN GENOME; NOMENCLATURE; NORMAL DISTRIBUTION; PROCEDURES; SEQUENCE ANALYSIS; STATISTICAL MODEL;

PROTEIN;

ALGORITHMS; AREA UNDER CURVE; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CLUSTER ANALYSIS; COMPUTATIONAL BIOLOGY; COMPUTING METHODOLOGIES; DATA INTERPRETATION, STATISTICAL; DATABASES, BIBLIOGRAPHIC; GENOME, HUMAN; HUMANS; MODELS, STATISTICAL; NAMES; NEURAL NETWORKS (COMPUTER); NORMAL DISTRIBUTION; PATTERN RECOGNITION, AUTOMATED; PROTEINS; ROC CURVE; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE;

EID: 25444518437     PISSN: 14712105     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-6-157     Document Type: Article

References (57)

1. Pubmed database [http://www.ncbi.nlm.nih.gov/PubMed/]
2. Shatkay H, Feldman R: Mining the Biomedical Literature in the Genomic Era: An Overview. Journal of Computational Biology 2003, 10:821-855.
3. Cohen KB, Hunter L: Natural language processing and systems biology. In Artificial intelligence and systems biology Edited by: Dubitzky W, Pereira F. Kluwer Academic Publishers; 2004.
4. Blaschke C, Andrade MA, Ouzounis C, Valencia A: Automatic Extraction of Biological Information from Scientific Text: Protein-Protein Interactions. In Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology Edited by: Lengauer T, Schneider R, Bork P, Brutlag D, Glasgow J, Mewes HW, Zimmer R. AAAI Press; 1999:60-67.
5. Ono T, Hishigaki H, Tanigami A, Takagi T: Automated extraction of information on protein-protein interactions from the biological literature. Bioinformatics 2001, 17:155-161.
6. Marcotte EM, Xenarios I, Eisenberg D: Mining literature for protein-protein interactions. Bioinformatics 2001, 17:359-363.
7. Temkin JM, Gilder MR: Extraction of protein interaction information from unstructured text using a context-free grammar. Bioinformatics 2003, 19:2046-2053.
8. Donaldson I, Martin J, de Bruijn B, Wolting C, Lay V, Tuekam B, Zhang S, Baskin B, Bader G, Michalickova K, Pawson T, Hogue C: PreBIND and Textomy - mining the biomedical literature for protein-protein interactions using a support vector machine. BMC Bioinformatics 2003, 4:11.
9. Daraselia N, Yuryev A, Egorov S, Novichkova S, Nikitin A, Mazo I: Extracting human protein interactions from MEDLINE using a full-sentence parser. Bioinformatics 2004, 20:604-611.
10. Ginter F, Pahikkala T, Pyysalo S, Boberg J, Järvinen J, Salakoski T: Extracting protein-protein interaction sentences by applying rough set data analysis. In Proceedings of the Fourth International Conference on Rough Sets and Current Trends in Computing, Lecture Notes in Computer Science 3066 Edited by: Tsumoto H, Slowinski R, Komorowski J, Grzymala-Busse JW. Springer-Verlag; 2004:780-785.
11. Fukuda K, Tsunoda T, Tamura A, Takagi T: Toward information extraction: Identifying protein names from biological papers. In Proceedings of the Pacific Symposium on Biocomputing Edited by: Altman R, Dunker A, Hunter L, Klein T. Singapore: World Scientific Press; 1998:707-718.
12. Nobata C, Collier N, Tsujii J: Automatic Term Identification and Classification in Biology Texts. Proceedings of the fifth Natural Language Processing Pacific Rim Symposium 1999:369-374.
13. Collier N, Nobata C, Tsujii J: Extracting the Names of Genes and Gene Products with a Hidden Markov Model. In Proceedings of the Eighteenth International Conference on Computational Linguistics Association for Computational Linguistics; 2000:201-207.
14. Tanabe L, Wilbur WJ: Tagging gene and protein names in biomedical text. Bioinformatics 2002, 18:1124-1132.
15. Yu H, Hatzivassiloglou V, Rzhetsky A, Wilbur WJ: Automatically identifying gene/protein terms in MEDLINE abstracts. Journal of Biomedical Informatics 2002, 35:322-330.
16. Franzén K, Eriksson G, Olsson F, Asker L, Lidén P, Cöster J: Protein Names And How To Find Them. International Journal of Medical Informatics 2002, 67:49-61.
17. Yu H, Agichtein E: Extracting synonymous gene and protein terms from biological literature. Bioinformatics 2003, 19(Suppl1): i340-i349.
18. Chang JT, Schütze H, Altman RB: GAPSCORE: finding gene and protein names one word at a time. Bioinformatics 2004, 20:216-225.
19. Zhou G, Zhang J, Su J, Shen D, Tan CL: Recognizing names in biomedical texts: a machine learning approach. Bioinformatics 2004, 20:1178-1190.
20. Lee KJ, Hwang YS, Kim S, Rim HC: Biomedical named entity recognition using two-phase model based on SVMs. Journal of Biomedical Informatics 2004, 37:436-447.
21. Collier N, Park HS, Ogata N, Tateisi Y, Nobata C, Sekimizu T, Imai H, Tsujii J: The GENIA project: corpus-based knowledge acquisition and information extraction from genome research papers. In Proceedings of the European Association for Computational Linguistics Edited by: Thompson HS, Lascarides A. Association for Computational Linguistics; 1999:271-272.
22. Kim J, Ohta T, Tsuruoka Y, Tateisi Y, Collier N: Introduction to the bio-entity recognition task at JNLPBA. Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications 2004:70-75.
23. Manning CD, Schütze H: Foundations of Statistical Natural Language Processing Cambridge, Massachusetts: The MIT Press; 1999.
24. Hatzivassiloglou V, Duboué AP, Rzhetsky A: Disambiguating proteins, genes and RNA in text: a machine learning approach. Bioinformatics 2001, 17:97-106.
25. Liu H, Aronson A, Friedman C: A Study of Abbreviations in MEDLINE Abstracts. In Proceedings of the 2002 AMIA Annual Symposium Edited by: Kohane IS. Hanley and Belfus; 2002:464-468.
26. Ginter F, Boberg J, Järvinen J, Salakoski T: New Techniques for Disambiguation in Natural Language and Their Application to Biological Text. Journal of Machine Learning Research 2004, 5:605-621.
27. Liu H, Johnson SB, Friedman C: Automatic Resolution of Ambiguous Terms Based on Machine Learning and Conceptual Relations in the UMLS. Journal of the American Medical Informatics Association 2002, 9:621-636.
28. Yu Z, Tsuruoka Y, Tsujii J: Automatic Resolution of Ambiguous Abbreviations in Biomedical Texts using Support Vector Machines and One Sense Per Discourse Hypothesis. In Proceedings of the SIGIR'03 Workshop on Text Analysis and Search for Bioinformatics Edited by: Brown E, Hersh W, Valencia A. ACM Press; 2003:57-62.
29. Podowski RM, Cleary JG, Goncharoff NT, Amoutzias G, Hayes WS: AZuRE, a Scalable System for Automated Term Disambiguation of Gene and Protein Names. In 3rd International IEEE Computer Society Computational Systems Bioinformatics Conference IEEE Computer Society; 2004:415-424.
30. Lee YK, Ng HT: An Empirical Evaluation of Knowledge Sources and Learning Algorithms for Word Sense Disambiguation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing Edited by: Hajič J, Matsumoto Y. Philadelphia: Association for Computational Linguistics; 2002:41-48.
31. Kazama J, Makino T, Ohta Y, Tsujii J: Tuning Support Vector Machines for Biomedical Named Entity Recognition. In ACL Workshop on Natural Language Processing in the Biomedical Domain Association for Computational Linguistics; 2002:1-8.
32. Takeuchi K, Collier N: Bio-Medical Entity Extraction using Support Vector Machines. Proceedings of the ACL 2003 Workshop on Natural Language Processing in Biomedicine 2003:57-64.
33. Lee KJ, Hwang YS, Rim HC: Two-Phase Biomedical NE Recognition based on SVMs. Proceedings of the ACL 2003 Workshop on Natural Language Processing in Biomedicine 2003:33-40.
34. Collier N, Takeuchi K: Comparison of character-level and part of speech features for name recognition in biomedical texts. Journal of Biomedical Informatics 2004, 37:423-435.
35. Zhou G: Recognizing Names in Biomedical Texts using Hidden Markov Model and SVM plus Sigmoid. Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications 2004:1-7.
36. Park KM, Kim SH, Lee DG, Rim HC: Incorporating Lexical Knowledge into Biomedical NE Recognition. Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications 2004:76-79.
37. Lee C, Hou WJ, Chen HH: Annotating Multiple Types of Biomedical Entities: A Single Word Classification Approach. Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications 2004:80-83.
38. Rössler M: Adapting an NER-System for German to the Biomedical Domain. Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications 2004:92-95.
39. Zhou G, Su J: Exploring deep knowledge resources in biomedical name recognition. Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications 2004:96-99.
40. Song Y, Kim E, Lee GG, Yi BK: POSBIOTM-NER in the Shared Task of BioNLP/NLPBA2004. Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications 2004:100-103.
41. Fawcett T: Roc graphs: Notes and practical considerations for data mining researchers. Tech Rep HPL-2003-4, HP Labs, Palo Alto, Ca 2003.
42. Boser BE, Guyon I, Vapnik V: A Training Algorithm for Optimal Margin Classifiers. In Proceedings of the Fifth Annual ACM Workshop on Computational Learing Theory Edited by: Haussler D. New York: ACM Press; 1992:144-152.
43. Burges CJC: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 1998, 2:121-167.
44. Vapnik VN: Statistical Learning Theory New York: Wiley; 1998.
45. Hsu CW, Chang CC, Lin CJ: A practical guide to support vector classification. Tech. rep., Department of Computer Science and Information Engineering, National Taiwan University, Taipei; 2003.
46. Yarowsky D: Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the Thirty-Third conference on Association for Computational Linguistics Edited by: Uszkoreit H. Association for Computational Linguistics; 1995:189-196.
47. Porter MF: An algorithm for suffix stripping. Program 1980, 14:130-137.
48. Maloof M: Learning when data sets are imbalanced and when costs are unequal and unknown. ICML-2003 Workshop on Learning from Imbalanced Data Sets II 2003.
49. Bradley AP: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 1997, 30:1145-1159.
50. Alpaydin E: Combined 5 × 2 cv F Test for Comparing Supervised Classification Learning Algorithms. Neural Computation 1999, 11:1885-1892.
51. Chang CC, Lin CJ: LIBSVM: a library for support vector machines. [http://www.csie.ntu.edu.tw/~cjlin/libsvm].
52. McCallum AK: Bow: a toolkit for statistical language modeling, text retrieval, classification and clustering. 1996 [http://www-2.cs.cmu.edu/~mccallum/bow/].
53. Keerthi SS, Lin CJ: Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation 2003, 15:1667-1689.
54. Lodhi H, Saunders C, Shawe-Taylor J, Cristianini N, Watkins C: Text Classification using String Kernels. Journal of Machine Learning Research 2002, 2:419-444.
55. Cancedda N, Gaussier E, Goutte C, Renders JM: Word-Sequence Kernels. Journal of Machine Learning Research 2003, 3:1059-1082.
56. Hastie T, Rosset S, Tibshirani R, Zhu J: The Entire Regularization Path for the Support Vector Machine. Journal of Machine Learning Research 2004, 5:1391-1415.
57. Vapnik VN: The nature of statistical learning theory Springer-Verlag New York, Inc; 1995.