Comparative experiments on learning information extractors for proteins and their interactions

Artificial Intelligence in Medicine

Volumn 33, Issue 2, 2005, Pages 139-155

  Bunescu, Razvan ^a   Ge, Ruifang ^a   Kate, Rohit J ^a   Marcotte, Edward M ^b   Mooney, Raymond J ^a   Ramani, Arun K ^b   Wong, Yuk Wah ^a  

^a University of Texas at Austin   (United States)

^b UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

Information extraction; Machine learning; Medline; Protein interactions; Text mining

Indexed keywords

DATA ACQUISITION; GENES; LEARNING SYSTEMS; PROTEINS;

BIOMEDICAL TEXTS; INFORMATION EXTRACTION; SUPPORT VECTOR MACHINES;

ARTIFICIAL INTELLIGENCE;

PROTEIN;

ARTICLE; AUTOMATION; GENE; GENE FUNCTION; INFORMATION RETRIEVAL; INFORMATION SYSTEM; MACHINE; MEDICAL LITERATURE; MEDLINE; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN FUNCTION; PROTEIN INTERACTION;

EID: 16244423656     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2004.07.016     Document Type: Article

References (56)

1. Fukuda K, Tsunoda T, Tamura A, Takagi T. Information extraction: identifying protein names from biological papers. In: Proceedings of the 3rd Pacific Symposium on Biocomputing. Kapalua, HI; 1998. p. 707 - 18.
2. Humphreys K, Demetriou G, and Geizauskas R Two applications of information extraction to biological science journal articles: enzyme interactions and protein structure Proceedings of the 5th Pacific Symposium on Biocomputing. Waikiki Beach, HI 2000 502 513
3. Blaschke C, and Valencia A Can bibliographic pointers for known biological data be found automatically? protein interactions as a case study Compar Funct Genom 2 2001 196 206
4. Proux D, Rechenmann F, and Julliard L A pragmatic information extraction strategy for gathering data on genetic interactions Proceedings of the 9th International Conference on Intelligent Systems for Molecular Biology (ISMB-2001). Copenhagen, Denmark 2000 279 285
5. Rindflesch TC, Tanabe L, Weinstein JN, and Hunter L EDGAR: extraction of drugs, genes, and relations from the biomedical literature Proceedings of the 5th Pacific Symposium on Biocomputing Waikiki Beach, HI 2000 515 524
6. Thomas J, Milward D, Ouzounis C, Pulman S, and Carol M Automatic extraction of protein interactions from scientific abstracts Proceedings of the 5th Pacific Symposium on Biocomputing Waikiki Beach, HI 2000 541 553
7. Hahn U, Romacker M, and Schulz S Creating knowledge repositories from biomedical reports: the MEDSYNDIKATE text mining system Proceedings of the 7th Pacific Symposium on Biocomputing Kauai, HI 2002 338 349
8. Pustejovsky J, Castano J, Zhang J, Kotecki M, and Cochran B Robust relational parsing over biomedical literature: extracting inhibit relations Proceedings of the 7th Pacific Symposium on Biocomputing Kauai, HI 2002 362 373
9. Park J, Kim HS, and Kim JJ Bidirectional incremental parsing for automatic pathway identification with combinatory categorial grammar Proceedings of the 6th Pacific Symposium on Biocomputing Mauna Lani, HI 2001 396 407
10. Friedman C, Kra P, Yu H, Krauthammer M, and Rzhetsky A GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles Mauna Lani, HI Bioinformatics 17 Suppl 1 2001 S74 S82
11. Cowie J, and Lehnert W Information extraction Commun Assoc Comput Machinery 39 1 1996 80 91
12. Cardie C Empirical methods in information extraction AI Mag 18 4 1997 65 79
13. Califf ME, editor. Papers from the AAAI-1999 Workshop on Machine Learning for Information Extraction. Orlando, FL: AAAI Press; 1999.
14. Proux D, Rechenmann F, Julliard L, Pillet V, and Jacq B Detecting gene symbols and names in biological texts: a first step toward pertinent information extraction Genome Inform 9 1998 72 80 (GIW'98)
15. Craven M, and Kumlien J Constructing biological knowledge bases by extracting information from text sources Proceedings of the 7th International Conference on Intelligent Systems for Molecular Biology (ISMB-1999) Heidelberg, Germany 1999 77 86
16. Collier N, No C, and Tsujii J Extracting the names of genes and gene products with a hidden Markov model Proceedings of the 18th International Conference on Computat Linguistics, Saarbrücken Germany 2000 201 207
17. Ray S, and Craven M Representing sentence structure in hidden Markov models for information extraction Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI-2001) Seattle, WA 2001 1273 1279
18. Eliassi-Rad T, and Shavlik J A theory-refinement approach to information extraction Proceedings of the 18th International Conference on Machine Learning (ICML-2001) Williamstown, MA 2001
19. Leonard JE, Colombe JB, and Levy JL Finding relevant references to genes and proteins in medline using a Bayesian approach Williamstown, MA Bioinformatics 18 11 2002 1515 1522
20. Raychaudhuri S, Chang JT, Sutphin PD, and Altman RB Associating genes with gene ontology codes using a maximum entropy analysis of biomedical literature Genome Res 12 2002 203 214
21. Perez-Iratxeta C, Bork P, and Andrade MA Association of genes to genetically inherited diseases using data mining Nat Genet 31 3 2002 316 319
22. Venter, JC, et al. The sequence of the human genome. Science 16 February 2001;291(5507):1304-51.
23. Lander, ES, et al. Initial sequencing and analysis of the human genome. Nature 15 February 2001;409(6822):860-921.
24. Califf ME, and Mooney RJ Relational learning of pattern-match rules for information extraction Proceedings of the 16th National Conference on Artificial Intelligence (AAAI-99) Orlando, FL 1999 328 334
25. Freitag D, and Kushmerick N Boosted wrapper induction Proceedings of the 17th National Conference on Artificial Intelligence (AAAI-2000) Orlando, FL 2000 AAAI Press/The MIT Press Austin, TX 577 583
26. Duda RO, and Hart PE Pattern classification and scene analysis 1973 Wiley New York
27. Brill E Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging Orlando, FL Computat Linguist 21 4 1995 543 565
28. Vapnik VN The nature of statistical learning theory 1995 Springer-Verlag Berlin
29. Berger AL, Della Pietra SA, and Della Pietra VJ A maximum entropy approach to natural language processing Computat Linguist 22 1 1996 39 71
30. Xenarios I, Fernandez E, Salwinski L, Duan XJ, Thompson MJ, Marcotte EM, and Eisenberg D DIP: The database of interacting proteins: 2001 update Nucleic Acids Res 29 1 2001 239 241
31. Bikel DM, Schwartz R, and Weischedel RM An algorithm that learns what's in a name Machine Learning 34 1999 211 232
32. Roth D, van den Bosch A, editors. Proceedings of the 6th Conference on Natural Language Learning (CoNLL-2002). Association for Computational Linguistics. Taipei, Taiwan; 2002.
33. Kushmerick N, Weld DS, and Doorenbos RB Wrapper induction for information extraction Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI-97) Nagoya, Japan 1997 729 735
34. Freund Y, and Schapire RE Experiments with a new boosting algorithm Saitta L Proceedings of the 13th International Conference on Machine Learning (ICML-96) Bari, Italy: Morgan Kaufmann 1996
35. Vapnik VN Statistical learning theory 1998 John Wiley & Sons New York
36. Joachims T Text categorization with support vector machines: learning with many relevant features Proceedings of the 10th European Conference on Machine Learning Bari, Italy: Morgan Kaufmann 1998 Springer-Verlag Berlin 137 142
37. Ramshaw LA, and Marcus MP Text chunking using transformation-based learning Proceedings of the 3rd Workshop on Very Large Corpora Cambridge, MA 1995
38. Kudoh T, and Matsumoto Y Use of support vector learning for chunk identification Proceedings of CoNLL-2000 and LLL-2000 Lisbon, Portugal 2000 142 144
39. Kubat M, Holte RC, and Matwin S Machine learning for the detection of oil spills in satellite radar images Lisbon, Portugal Machine Learning 30 2-3 1998 195 215
40. Collins M Ranking algorithms for named-entity extraction: boosting and the voted perceptron Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL-02) 2002
41. Rabiner LR A tutorial on hidden Markov models and selected applications in speech recognition Proc IEEE 77 2 1989 257 286
42. Darroch J, and Ratchliff D Generalized iterative scaling for log-linear models Ann Math Stat 43 5 1972 1470 1480
43. Tanabe L, and Wilbur WJ Tagging gene and protein names in biomedical text Bioinformatics 18 8 2002 1124 1132
44. Marcus M, Santorini B, and Marcinkiewicz MA Building a large annotated corpus of English: the Penn treebank Computat Linguist 19 2 1993 313 330
45. National Institute of Standards and Technology, ACE - automatic content extraction (http://www.nist.gov/speech/tests/ace/)
46. Marcotte E, Xenarios I, and Eisenberg D Mining literature for protein-protein interactions Bioinformatics April 17 4 2001 359 363
47. Blaschke C, and Valencia A The frame-based module of the Suiseki information extraction system IEEE Intell Sys 17 2002 14 20
48. Charras C, Lecroq T. Sequence comparison, Laboratoire d'Informatique de Rouen et Atelier Biologie Informatique Statistique Socio-Linguistique, Universit'e de Rouen, France; 1998 (http://www-igm.univ-mlv.fr/lecroq/seqcomp)
49. Gusfield D Algorithms on strings, trees and sequences 1997 Cambridge University Press New York
50. Levenshtein VI Binary codes capable of correcting insertions and reversals Sov Phys Doklady 10 8 1966 707 710
51. Cestnik B Estimating probabilities: a crucial task in machine learning Proceedings of the 9th European Conference on Artificial Intelligence Stockholm, Sweden 1990 147 149
52. Pearl J Probabilistic reasoning in intelligent systems: networks of plausible inference 1988 Morgan Kaufmann San Mateo,CA
53. Thompson CA, Califf ME, and Mooney RJ Active learning for natural language parsing and information extraction Proceedings of the 16th International Conference on Machine Learning (ICML-99) Bled, Slovenia 1999 406 414
54. M. Collins, Y. Singer, Unsupervised models for named entity classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC-99). University of Maryland, College Park, MD; 1999.
55. McCallum A, Freitag D, and Pereira F Maximum entropy Markov models for information extraction and segmentation Proceedings of the 17th International Conference on Machine Learning (ICML-2000) Stanford, CA 2000
56. J. Lafferty, A. McCallum, F. Pereira, Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: ICML-2001. Williamstown, MA; 2001.