Cost sensitive hierarchical document classification to triage PubMed abstracts for manual curation

BMC Bioinformatics

Volumn 12, Issue 1, 2011, Pages

  Seymour, Emily ^a   Damle, Rohini ^a   Sette, Alessandro ^a   Peters, Bjoern ^a  

^a LA JOLLA INSTITUTE FOR ALLERGY AND IMMUNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DATASETS; CLASSIFICATION PERFORMANCE; HIERARCHICAL APPLICATIONS; HIERARCHICAL DOCUMENT; MISCLASSIFICATIONS; PREDICTION ACCURACY; SCIENTIFIC ARTICLES; SENSITIVITY FUNCTIONS;

ABSTRACTING; BENCHMARKING; CLASSIFIERS; COSTS; DATABASE SYSTEMS; EPITOPES; INFORMATION RETRIEVAL SYSTEMS;

CLASSIFICATION (OF INFORMATION);

EPITOPE;

ALGORITHM; ARTICLE; BAYES THEOREM; CLASSIFICATION; FACTUAL DATABASE; HUMAN; INFORMATION SYSTEM; MEDLINE; SUPPORT VECTOR MACHINE;

ALGORITHMS; BAYES THEOREM; DATABASES, FACTUAL; EPITOPES; HUMANS; INFORMATION SYSTEMS; PUBMED; SUPPORT VECTOR MACHINES;

EID: 83655197158     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-12-482     Document Type: Article

References (45)

1. Peters B, Sidney J, Bourne P, Bui HH, Doh G, Fleri W, Kronenberg M, Kubo R, Lund O, Nemazee D, Ponomarenko JV, Sathiamurthy M, Schoenberger S, Stewart S, Surko P, Way S, Wilson S, Sette A. The Immune Epitope Database and Analysis Resource: from vision to blueprint. PLoS Biology 2005, 3(3):379-381.
2. Vita R, Zarebski L, Greenbaum JA, Emami H, Hoof I, Salimi N, Damle R, Sette A, Peters B. The Immune Epitope Database 2.0. Nucleic Acids Research 2010, (38 Database):D854-D862.
3. Vita R, Peters B, Sette A. The curation guidelines of the Immune Epitope Database and Analysis Resource. Cytometry A 2008, 73(11):1066-1070. 2597159, 18688821.
4. Wang P, Morgan AA, Zhang Q, Sette A, Peters B. Automating document classification for the Immune Epitope Database. BMC Bioinformatics 2007, 8:269. 10.1186/1471-2105-8-269, 1965490, 17655769.
5. Davies V, Vaughan K, Damle R, Peters B, Sette A. Classification of the universe of immune epitope literature: representation and knowledge gaps. PLoS One 2009, 4(9):e6948. 10.1371/journal.pone.0006948, 2747625, 19774228.
6. Yang Y, Liu X. A re-examination of text categorization methods. Proceedings of the 22nd Annual International ACM SIGR Conference on Research and Development in Information Retrieval: 15 August 1999; Berkeley, California 1999, 42-49. Marti Herst, and Richard Tong: ACM, Fredric Gey.
7. Chakrabarti S, Dom B, Agrawal R, Raghavan P. Using taxonomy, discriminants, and signatures for navigating in text databases. Proceedings of the 23rd International Conference on Very Large Data Bases: 24-27 August 1997; Athens, Greece 1997, 446-455. Jeusfeld: Morgan Kaufmann, Matthias Jarke, Michael J Carey, Klaus R Dittrich, Frederick H Lochovsky Pericles Loucopoulos, and Manfred A.
8. Wang K, Zhou S, Liew SC. Building hierarchical classifiers using class proximity. Proceedings of the 25th International Conference on Very Large Data Bases: 7-10 September 1999; Edinburgh, Scotland 1999, 363-374. Brodie: Morgan Kaufmann, Malcolm P Atkinson, Maria E Orlowska, Patrick Valduriez, Stanley B Zdonik, and Michael L.
9. Wang K, Zhou S, He Y. Hierarchical classification of real life documents. Proceedings of the 1st SIAM International Conference on Data Mining: 5-7 April 2001; Chicago, Illinois 2001, 1-16. SIAM, Vipin Kumar and Robert Grossman.
10. Koller D, Sahami M. Hierarchically classifying documents using very few words. Proceedings of the 14th International Conference on Machine Learning: 8-12 July 1997; Nashville, Tennessee 1997, 170-178. Fisher: Morgan Kaufmann, Douglas H.
11. D'Alessio S, Murray K, Schiaffino R, Kershenbaum A. The effect of using hierarchical classifiers in text categorization. Proceedings of the 6th International Conference on Computer-Assisted Information Retrieval: 12-14 April 2000; Paris, France 2000, 302-313. CID, Joseph-Jean Mariani and Donna Harman.
12. Hao PY, Chiang JH, Tu YK. Hierarchically SVM classification based on support vector clustering method and its application to document categorization. Expert Systems with Applications 2007, 33(3):627-635.
13. Dumais S, Chen H. Hierarchical classification of web content. Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval: 24-28 July 2000; Athens, Greece 2000, 256-263. Belkin, Peter Ingwersen, and Mun-Kew Leong: ACM, Nicholas J.
14. Torii M, Liu H. Classifier ensemble for biomedical document retrieval. Short Paper Proceedings of the 2nd International Symposium on Languages in Biology and Medicine: 6-7 December 2007; Singapore 2007, 5.1-5.17. Jian: CEUR Workshop Proceedings, Christopher JO Baker and Sun., http://ceur-ws.org/Vol-319/Paper5.pdf
15. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: an update. SIGKDD Explorations 2009, 11(1):10-18.
16. Weka Docs. , http://weka.sourceforge.net/doc/weka/classifiers/functions/MultilayerPerceptron.html
17. Cortes C, Vapnik V. Support-vector networks. Machine Learning 1995, 20(3):273-297.
18. Joachims T. Text categorization with support vector machines: learning with many relevant features. Proceedings of the 10th European Conference on Machine Learning: 21-23 April 1998; Chemnitz, Germany 1998, 137-142. Springer-Verlag, Claire Nedellac and Celine Rouveirol.
19. Sebastiani F. Machine learning in automated text categorization. ACM Computing Surveys (CSUR) 2002, 34(1):1-47.
20. Akbani R, Kwek S, Japkowicz N. Applying support vector machines to imbalanced datasets. 15th European Conference on Machine Learning: 20-24 September 2004; Pisa, Italy 2004, 39-50. Springer-Verlag, Jean-Francois Boulicaut, Floriana Esposito, Fosca Giannotti, and Dino Pedreschi.
21. Lin Y, Lee Y, Wahba G. Support vector machines for classification in nonstandard situations. Machine Learning 2002, 46(1-3):191-202.
22. Cai L, Hofmann T. Hierarchical document categorization with support vector machines. Proceedings of the 13th ACM International Conference on Information and Knowledge Management: 8-13 November 2004; Washington, D.C 2004, 78-87. Evans: ACM, David A Grossman, Luis Gravano, Cheng Xiang Zhai, Otthein Herzog, and David A.
23. Hunter L, Cohen KB. Biomedical language processing: perspective what's beyond PubMed?. Molecular Cell 2006, 21(5):589-594. 10.1016/j.molcel.2006.02.012, 1702322, 16507357.
24. Cohen AM, Hersh WR. A survey of current work in biomedical text mining. Briefings in Bioinformatics 2005, 6(1):57-71. 10.1093/bib/6.1.57, 15826357.
25. Greenbaum JA, Kotturi MF, Kim Y, Oseroff C, Vaughan K, Salimi N, Vita R, Ponomarenko J, Scheuermann RH, Sette A, Peters B. Pre-existing immunity against swine-origin H1N1 influenza viruses in the general human population. Proceedings of the National Academy of Sciences 2009, 106(48):20365-20370.
26. Vivona S, Gardy JL, Ramachandran S, Brinkman FSL, Raghava GPS, Flower DR, Filippini F. Computer-aided biotechnology: from immuno-informatics to reverse vaccinology. Trends in Biotechnology 2008, 26(4):190-200. 10.1016/j.tibtech.2007.12.006, 18291542.
27. Agarwal P, Searls DB. Literature mining in support of drug discovery. Briefings in Bioinformatics 2008, 9(6):479-492. 10.1093/bib/bbn035, 18820304.
28. Poulter GL, Rubin DL, Altman RB, Seoighe C. MScanner: a classifier for retrieving Medline citations. BMC Bioinformatics 2008, 9:108. 10.1186/1471-2105-9-108, 2263023, 18284683.
29. Tong JC, Ren EC. Immunoinformatics: current trends and future directions. Drug Discovery Today 2009, 14(13-14):684-689. 10.1016/j.drudis.2009.04.001, 19379830.
30. Salimi N, Fleri W, Peters B, Sette A. Design and utilization of epitope-based databases and predictive tools. Immunogenetics 2010, 62(4):185-196. 10.1007/s00251-010-0435-2, 2843836, 20213141.
31. Krallinger M, Rojas AM, Valencia A. Creating reference datasets for systems biology applications using text mining. Annals of the New York Academy of Sciences 2009, 1158:14-28. 10.1111/j.1749-6632.2008.03750.x, 19348628.
32. Noto K, Saier M, Elkan C. Learning to find relevant biological articles without negative training examples. Proceedings of 21st Australasian Joint Conference on Artificial Intelligence: 1-5 December 2008; Auckland, New Zealand. Lecture Notes in Computer Science 2008, 202-213. Springer, Wayne Wobcke and Mengjie Zhang.
33. Lourenco A, Carreira R, Glez-Pena D, Mendez JR, Carneiro S, Rocha LM, Diaz F, Ferreira EC, Rocha I, Rocha M. BioDR: semantic indexing networks for biomedical document retrieval. Expert Systems with Applications 2010, 37(4):3444-3453.
34. Kastrin A, Peterlin B, Hristovski D. Chi-square-based scoring function for categorization of MEDLINE citations. Methods Inf Med 2010, 49(4):371-378. 10.3414/ME09-01-0009, 20091016.
35. Duchrow T, Shtatland T, Guettler D, Pivovarov M, Kramer S, Weissleder R. Enhancing navigation in biomedical databases by community voting and database-driven text classification. BMC Bioinformatics 2009, 10:317. 10.1186/1471-2105-10-317, 2768718, 19799796.
36. Duchrow T. Peptide sequence named entity recognition and database-driven text classification. Diploma thesis 2008, Technische Universitat Munchen.
37. Lourenco A, Carneiro S, Ferreira EC, Carreira R, Rocha LM, Glez-Pena D, Mendez JR, Fdez-Riverola F, Diaz F, Rocha I, Rocha M. Biomedical text mining applied to document retrieval and semantic indexing. Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living: Lecture Notes in Computer Science: 10-12 June 2009; Salamanca, Spain 2009, 954-963. Corchado: Springer-Verlag, Sigeru Omatu, Miguel Rocha, Jose Bravo, Florentino Fernandez Riverola, Emilio Corchado, Andres Bustillo, and Juan M.
38. Joachims T. SVM light Support Vector Machine. , http://svmlight.joachims.org
39. PubMed Help. , http://www.ncbi.nlm.nih.gov/books/NBK3827/?rendertype=table&id=pubmedhelp.T43
40. Gardner MW, Dorling SR. Artificial neural networks (the Multilayer Perceptron) - a review of applications in the atmospheric sciences. Atmospheric Environment 1998, 32(14-15):2627-2636.
41. Pal SK, Mitra S. Multilayer Perceptron, fuzzy sets, and classification. IEEE Transactions on Neural Networks 1992, 3(5):683-697. 10.1109/72.159058, 18276468.
42. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence: 20-25 August 1995; Montreal, Canada 1995, 1137-1145. Mellish: Morgan Kaufmann, Chris S.
43. Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 1997, 30:1145-1159.
44. Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983, 148(3):839-843.
45. Fawcett T. An introduction to ROC analysis. Pattern Recognition Letters 2006, 27:861-874.