Protein interaction sentence detection using multiple semantic kernels

Journal of Biomedical Semantics

Volumn 2, Issue 1, 2011, Pages

  Polajnar, Tamara ^a   Damoulas, Theodoros ^b   Girolami, Mark ^c  

^a UNIVERSITY OF GLASGOW   (United Kingdom)

^b Cornell University ^*  (United States)

^c UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 82255168153     PISSN: None     EISSN: 20411480     Source Type: Journal    
DOI: 10.1186/2041-1480-2-1     Document Type: Article

References (74)

1. Young KH: Yeast two-hybrid: so many interactions, (in) so little time. Biol Reprod. 1998, 58 (2): 302-311. 10.1095/biolreprod58.2.302. [ http://www.hubmed.org/display.cgi?uids=9475380 ]
2. Nanas N, Roeck AND, Vavalis M: What Happened to Content-Based Information Filtering?. ICTIR 2009, Volume 5766 of Lecture Notes in Computer Science. Edited by: Azzopardi L, Kazai G, Robertson SE, Rüger SM, Shokouhi M, Song D, Yilmaz E. 2009, Springer, 249-256.
3. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M: KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 2010, 355-360. [ http://www.hubmed.org/display.cgi?uids=19880382 ]38 Database
4. Mewes HW, Amid C, Arnold R, Frishman D, Güldener U, Mannhaupt G, Münsterkötter M, Pagel P, Strack N, Stümpen V, Warfsmann J, Ruepp A: MIPS: analysis and annotation of proteins from whole genomes. Nucleic Acids Res. 2004, 41-44. [ http://www.hubmed.org/display.cgi?uids=14681354 ]32 Database
5. Alfarano C, Andrade CE, Anthony K, Bahroos N, Bajec M, Bantoft K, Betel D, Bobechko B, Boutilier K, Burgess E, Buzadzija K, Cavero R, D'Abreo C, Donaldson I, Dorairajoo D, Dumontier MJ, Dumontier MR, Earles V, Farrall R, Feldman H, Garderman E, Gong Y, Gonzaga R, Grytsan V, Gryz E, Gu V, Haldorsen E, Halupa A, Haw R, Hrvojic A, Hurrell L, Isserlin R, Jack F, Juma F, Khan A, Kon T, Konopinsky S, Le V, Lee E, Ling S, Magidin M, Moniakis J, Montojo J, Moore S, Muskat B, Ng I, Paraiso JP, Parker B, Pintilie G, Pirone R, Salama JJ, Sgro S, Shan T, Shu Y, Siew J, Skinner D, Snyder K, Stasiuk R, Strumpf D, Tuekam B, Tao S, Wang Z, White M, Willis R, Wolting C, Wong S, Wrong A, Xin C, Yao R, Yates B, Zhang S, Zheng K, Pawson T, Ouellette BF, Hogue CW: The Biomolecular Interaction Network Database and related tools 2005 update. Nucleic Acids Res. 2005, 418-424. [ http://www.hubmed.org/display.cgi?uids=15608229 ]33 Database
6. Cohen AM, Hersh WR: A survey of current work in biomedical text mining. Briefings in Bioinformatics. 2005, 6: 51-71.
7. Donaldson I, Martin J, de Bruijn B, Wolting C, Lay V, Tuekam B, Zhang S, Baskin B, Bader GD, Michalickova K, Pawson T, Hogue CW: PreBIND and Textomy - mining the biomedical literature for protein-protein interactions using a support vector machine. BMC Bioinformatics. 2003, 4 (11): [ http://www.biomedcentral.com/1471-2105/4/11 ]
8. Alex B, Grover C, Haddow B, Kabadjov M, Klein E, Matthews M, Roebuck S, Tobin R, Wang X: Assisted Curation: Does Text Mining Really Help?. Proceedings of Pacific Symposium on Biocomputing, Hawaii, USA. 2008
9. Jenssen TK, Laegreid A, Komorowski J, Hovig E: A literature network of human genes for high-throughput analysis of gene expression. Nat Genet. 2001, 28: 21-28. [ http://www.hubmed.org/display.cgi?uids=11326270 ]
10. Chen H, Sharp BM: Content-rich biological network constructed by mining PubMed abstracts. BMC Bioinformatics. 2004, 5: 147-147. 10.1186/1471-2105-5-147. [ http://www.hubmed.org/display.cgi?uids=15473905 ]
11. Rinaldi F, Schneider G, Kaljurand K, Hess M, Andronis C, Konstandi O, Persidis A: Mining of relations between proteins over biomedical scientific literature using a deep-linguistic approach. Artif Intell Med. 2007, 39 (2): 127-136. 10.1016/j.artmed.2006.08.005. [ http://www.hubmed.org/display.cgi?uids=17052900 ]
12. Koster C, Seibert O, Seutter M: The PHASAR Search Engine. Proceedings NLDB 2006, Springer LNCS 3999. 2006, 141-152.
13. Marcotte EM, Xenarios I, Eisenberg D: Mining literature for protein-protein interactions. Bioinformatics. 2001, 17: 359-363. 10.1093/bioinformatics/17.4.359.
14. Giuliano C, Lavelli A, Romano L: Exploiting Shallow Linguistic Information for Relation Extraction from Biomedical Literature. Proceedings of EACL 2006. 2006, 401-408.
15. Rosario B, Hearst M: Multi-way Relation Classification: Application to Protein-Protein Interaction. Proceedings of HLT-NAACL'05. 2005, [ http://biotext.berkeley.edu/papers/hlt-emnlp05-rosario.pdf ]
16. Thomas J, Milward D, Ouzounis C, Pulman S, Carroll M: Automatic Extraction of Protein Interactions from Scientific Abstracts. Pacific Symposium on Biocomputing 5. 2000, 538-549.
17. Miyao Y, Ohta T, Masuda K, Tsuruoka Y, Yoshida K, Ninomiya T, Tsujii J: Semantic retrieval for the accurate identification of relational concepts in massive textbases. ACL-44: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics. 2006, Morristown, NJ, USA: Association for Computational Linguistics, 1017-1024.
18. Rebholz-Schuhmann D, Kirsch H, Arregui M, Gaudan S, Riethoven M, Stoehr P: EBIMed-text crunching to gather facts for proteins from Medline. Bioinformatics. 2007, 23 (2): 237-244. 10.1093/bioinformatics/btl302. [ http://www.hubmed.org/display.cgi?uids=17237098 ]
19. Katrenko S, Adriaans P: Learning Relations from Biomedical Corpora Using Dependency Trees. Knowledge Discovery and Emergent Complexity in BioInformatics, Lecture Notes in Computer Science. 2006, 4366:
20. Bunescu R, Ge R, Kate RJ, Marcotte EM, Mooney RJ, Ramani AK, Wong YW: Comparative experiments on learning information extractors for proteins and their interactions. Artif Intell Med. 2005, 33 (2): 139-155. 10.1016/j.artmed.2004.07.016. [ http://www.hubmed.org/display.cgi?uids=15811782 ]
21. Erkan G, Özgür A, Radev D: Semi-Supervised Classifition for Extracting Protein Interaction Sentences using Dependency Parsing. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL). 2007, 228-237. [ http://www.aclweb.org/anthology/D/D07/D07-1024 ]
22. Cussens J, Nédellec C, (Eds): Proceedings of the 4th Learning Language in Logic Workshop (LLL05), Bonn. 2005, [ http://www.cs.york.ac.uk/aig/lll/lll05/proceedings.pdf ]
23. Airola A, Pyysalo S, Björne J, Pahikkala T, Ginter F, Salakoski T: All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning. BMC bioinformatics. 2008, 9 (Suppl 11): [ http://dx.doi.org/10.1186/1471-2105-9-S11-S2 ]
24. Pyysalo S, Airola A, Heimonen J, Björne J, Ginter F, Salakoski T: Comparative analysis of five protein-protein interaction corpora. BMC Bioinformatics. 2008, 9 (Suppl 3): S6-10.1186/1471-2105-9-S3-S6. [ http://www.biomedcentral.com/1471-2105/9/S3/S6 ]
25. Lewis DD: Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval. ECML '98: Proceedings of the 10th European Conference on Machine Learning. 1998, London, UK: Springer-Verlag, 4-15.
26. Tikk D, Thomas P, Palaga P, Hakenberg J, Leser U: A Comprehensive Benchmark of Kernel Methods to Extract Protein-Protein Interactions from Literature. PLoS Comput Biol. 2010, 6 (7): e1000837-10.1371/journal.pcbi.1000837. [ http://dx.doi.org/10.1371%2Fjournal.pcbi.1000837 ]
27. Clegg AB, Shepherd AJ: Benchmarking natural-language parsers for biological applications using dependency graphs. BMC Bioinformatics. 2007, 8: 24-24. 10.1186/1471-2105-8-24. [ http://www.hubmed.org/display.cgi?uids=17254351 ]
28. Albert S, Gaudan S, Knigge H, Raetsch A, Delgado A, Huhse B, Kirsch H, Albers M, Rebholz-Schuhmann D, Koegl M: Computer-assisted generation of a protein-interaction database for nuclear receptors. Journal of Molecular Endocrinology. 2003, 8 (17): 1555-67. [ http://www.ebi.ac.uk/Rebholz/publications.html ]
29. Hirschman L, Morgan AA, Yeh AS: Rutabaga by any other name: extracting biological names. J Biomed Inform. 2002, 35 (4): 247-259. 10.1016/S1532-0464(03)00014-5. [ http://www.hubmed.org/display.cgi?uids=12755519 ]
30. Subramaniam LV, Mukherjea S, Kankar P, Srivastava B, Batra VS, Kamesam PV, Kothari R: Information extraction from biomedical literature: methodology, evaluation and an application. CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management. 2003, New York, NY, USA: ACM Press, 410-417.
31. Tanabe L, Xie N, Thom LH, Matten W, Wilbur WJ: GENETAG: a tagged corpus for gene/protein named entity recognition. BMC Bioinformatics. 2005, 6 (Suppl 1): [ http://www.hubmed.org/display.cgi?uids=15960837 ]
32. Alex B, Haddow B, Grover C: Recognising Nested Named Entities in Biomedical Text. Biological, translational, and clinical language processing, Prague, Czech Republic: Association for Computational Linguistics. 2007, 65-72. [ http://www.aclweb.org/anthology/W/W07/W07-1009 ]
33. Smith L, Tanabe LK, Ando RJ, Kuo CJ, Chung IF, Hsu CN, Lin YS, Klinger R, Friedrich CM, Ganchev K, Torii M, Liu H, Haddow B, Struble CA, Povinelli RJ, Vlachos A, Baumgartner WA, Hunter L, Carpenter B, Tsai RT, Dai HJ, Liu F, Chen Y, Sun C, Katrenko S, Adriaans P, Blaschke C, Torres R, Neves M, Nakov P, Divoli A, Maña-López M, Mata J, Wilbur WJ: Overview of BioCreative II gene mention recognition. Genome Biol. 2008, 9 (Suppl 2): [ http://www.hubmed.org/display.cgi?uids=18834493 ]
34. Polajnar T, Rogers S, Girolami M: Classification of Protein Interaction Sentences via Gaussian Processes. Proceedings of 4th IAPR International Conference, Pattern Recognition in Bioinformatics. 2009, Springer Verlag, 282-292.
35. Sekimizu T, Park H, Tsujii J: Identifying the interaction between genes and gene products based on frequently seen verbs MEDLINE abstracts. 1998, [ http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.66 ]
36. Friedman C, Kra P, Yu H, Krauthammer M, Rzhetsky A: GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles. Bioinformatics. 2001, 17 (Suppl 1): S74-S82. 10.1093/bioinformatics/17.suppl_1.S74.
37. Polajnar T, Girolami M: Semi-supervised Prediction of Protein Interaction Sentences Exploiting Semantically Encoded Metrics. Proceedings of the 4th IAPR International Conference, Pattern Recognition in Bioinformatics. 2009, Springer Verlag, 270-281.
38. Chapelle O, Schölkopf B, Zien A, (Eds): Semi-Supervised Learning. 2006, Cambridge, MA: MIT Press, [ http://www.kyb.tuebingen.mpg.de/ssl-book ]
39. Abney S: Semisupervised Learning for Computational Linguistics. 2007, Chapman & Hall/CRC
40. Polajnar T: Semantic Models as Metrics for Kernel-based Interaction Identification. PhD thesis. 2010, University of Glasgow
41. Kim JD, Ohta T, Tateisi Y, Tsujii J: GENIA corpus-semantically annotated corpus for bio-textmining. Bioinformatics. 2003, 19 (Suppl 1): 180-182. 10.1093/bioinformatics/btg1023. [ http://www.hubmed.org/display.cgi?uids=12855455 ]
42. Lund K, Burgess C: Producing high-dimensional semantic spaces from lexical CO-occurrence. Behavior Research Methods, Instrumentation, and Computers. 1996, 28 (2): 203-208. 10.3758/BF03204766.
43. Jones MN, Mewhort DJK: Representing word meaning and order information in a composite holographic lexicon. Psychological Review. 2007, 114: 1-37.
44. Lanckriet GRG, Cristianini N, Bartlett P, Ghaoui LE, Jordan MI: Learning the Kernel Matrix with Semidefinite Programming. Journal of Machine Learning Research. 2004, 5: 27-72.
45. Bach F, Lanckriet G, Jordan M: Multiple kernel learning, conic duality, and the SMO algorithm. ICML '04: Proceedings of the twenty-first international conference on Machine learning, ACM. 2004
46. Girolami M, Rogers S: Hierarchic Bayesian Models for Kernel Learning. ICML '05: Proceedings of the 22nd international conference on Machine learning. 2005, New York, NY, USA: ACM, 241-248.
47. Bach F: Exploring large feature spaces with hierarchical multiple kernel learning. Advances in neural information processing systems 20: proceedings of the 2007 conference. 2008
48. Lanckriet GRG, Bie TD, Cristianini N, Jordan MI, Noble WS: A statistical framework for genomic data fusion. Bioinformatics. 2004, 20 (16): 2626-2635. 10.1093/bioinformatics/bth294.
49. Damoulas T, Girolami MA: Probabilistic multi-class multi-kernel learning: On protein fold recognition and remote homology detection. Bioinformatics. 2008, [ http://www.hubmed.org/display.cgi?uids=18378524 ]
50. PMKL Software. [ http://www.dcs.gla.ac.uk/inference/pMKL ]
51. McCullagh P, Nelder JA: Generalised Linear Models. 1989, London: Chapman & Hall
52. Padó S, Lapata M: Dependency-Based Construction of Semantic Space Models. Comput Linguist. 2007, 33 (2): 161-199. 10.1162/coli.2007.33.2.161.
53. McMurray B: Moo-cow! Mummy! More! How do children learn so many words?. Significance. 2007, 4 (4): 159-163. 10.1111/j.1740-9713.2007.00258.x.
54. Lowe W: Towards a theory of semantic space. Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society. Edited by: Moore JD, Stenning K. 2001, Mahwah NJ: Lawrence Erlbaum Associates, 576-581.
55. Landauer TK, Foltz PW, Laham D: An Introduction to Latent Semantic Analysis. Discourse Processes. 1998, 25: 259-284. 10.1080/01638539809545028.
56. Lin D: Automatic Retrieval and Clustering of Similar Words. Proceedings of the Joint Annual Meeting of the Association for Computational Linguistics and International Conference on Computational Linguistics, Montreal, Canada. 1998, 768-774.
57. Burgess C, Livesay K, Lund K: Explorations in context space: Words, sentences, discourse. Discourse Processes. 1998, 25: 211-257. 10.1080/01638539809545027.
58. Jones MN, Kintsch W, Mewhort DJ: High-dimensional semantic space accounts of priming. Journal of Memory and Language. 2006, 55 (4): 534-552. 10.1016/j.jml.2006.07.003. [ http://dx.doi.org/10.1016/j.jml.2006.07.003 ]
59. Burgess C, Lund K: Modeling parsing constraints with high-dimensional context space. Language and Cognitive Processes. 1997, 12: 177-210. 10.1080/016909697386844.
60. Song D, Bruza PD: Discovering Information Flow Using a High Dimensional Conceptual Space. Proceedings of ACM SIGIR 2001. 2001, 327-333.
61. Papadimitriou CH, Raghavan P, Tamaki H, Vempala S: Latent semantic indexing: a probabilistic analysis. Journal of Computer and System Sciences. 2000, 61 (2): 217-235. 10.1006/jcss.2000.1711.
62. Bingham E, Mannila H: Random projection in dimensionality reduction: applications to image and text data. KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. 2001, New York, NY, USA: ACM, 245-250.
63. Fradkin D, Madigan D: Experiments with random projections for machine learning. KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. 2003, New York, NY, USA: ACM, 517-522.
64. Kaski S: Dimensionality reduction by random mapping: fast similarity computation for clustering. Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. 1998, 1: 413-418.
65. Jin X, Bie R: Improving Software Quality Classification with Random Projection. Proceedings of ICCI 2006: 5th IEEE International Conference on Cognitive Informatics. 2006, 1: 149-154.
66. Achlioptas D: Database-friendly random projections. PODS '01: Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. 2001, New York, NY, USA: ACM, 274-281.
67. Saetre R, Sagae K, Tsujii J: Syntactic features for protein-protein interaction extraction. Proceedings of LBM'07, volume 319. 2008
68. Bouckaert RR, Frank E: Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms. Advances in Knowledge Discovery and Data Mining, Volume 3056 of Lecture Notes in Computer Science. Edited by: Dai H, Srikant R, Zhang C. 2004, Springer Berlin/Heidelberg, 3-12. [ http://dx.doi.org/10.1007/978-3-540-24775-3%5F3 ]
69. Cornell University Stop Word List. [ ftp://ftp.cs.cornell.edu/pub/smart/english.stop ]
70. Biomed Central Open Access Articles. [ http://www.biomedcentral.com/info/about/datamining/ ]
71. Azzopardi L, Girolami M, Crowe M: Probabilistic hyperspace analogue to language. SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval. 2005, New York, NY, USA: ACM, 575-576.
72. Fellbaum C, (Ed): WordNet: An Electronic Lexical Database. 1998, Cambridge, MA: MIT Press
73. Basili R, Cammisa M, Moschitti A: A Semantic Kernel to Exploit Linguistic Knowledge. AI*IA 2005: Advances in Artificial Intelligence. 2005, 290-302.
74. Minier Z, Bodo Z, Csato L: Wikipedia-Based Kernels for Text Categorization. SYNASC '07: Proceedings of the Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. 2007, Washington, DC, USA: IEEE Computer Society, 157-164.