Classification of protein-protein interaction full-text documents using text and citation network features

IEEE/ACM Transactions on Computational Biology and Bioinformatics

Volumn 7, Issue 3, 2010, Pages 400-411

  Kolchinsky, Artemy ^a,b   Abi Haidar, Alaa ^a,b   Kaur, Jasleen ^a   Hamed, Ahmed Abdeen ^a   Rocha, Luis M ^a,b  

^a INDIANA UNIVERSITY   (United States)

^b Instituto Gulbenkian de Ciencia ^*  (Portugal)

Author keywords

binary classification; citation network; literature mining; protein protein interaction; Text mining

Indexed keywords

BINARY CLASSIFICATION; CITATION NETWORKS; LITERATURE MINING; PROTEIN-PROTEIN INTERACTIONS; TEXT MINING;

CLASSIFIERS; INFORMATION RETRIEVAL SYSTEMS; NATURAL LANGUAGE PROCESSING SYSTEMS; PLASMA WAVES; PROTEINS; TECHNICAL WRITING;

TEXT PROCESSING;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIBLIOGRAPHIC DATABASE; BIOLOGY; CLASSIFICATION; DATA MINING; DOCUMENTATION; METHODOLOGY; PROTEIN ANALYSIS; PUBLICATION;

ABSTRACTING AND INDEXING AS TOPIC; ALGORITHMS; COMPUTATIONAL BIOLOGY; DATA MINING; DATABASES, BIBLIOGRAPHIC; NEURAL NETWORKS (COMPUTER); PERIODICALS AS TOPIC; PROTEIN INTERACTION MAPPING;

EID: 77955471310     PISSN: 15455963     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCBB.2010.55     Document Type: Article

References (33)

1. L. Hunter and K. Cohen, "Biomedical Language Processing: What's Beyond Pubmed?" Molecular Cell, vol.21, no.5, pp. 589-594, 2006.
2. Pubmed, http://www.pubmed.com, 2010.
3. H. Shatkay and R. Feldman, "Mining the Biomedical Literature in the Genomic Era: An Overview," J. Computational Biology, vol.10, no.6, pp. 821-856, 2003.
4. L.J. Jensen, J. Saric, and P. Bork, "Literature Mining for the Biologist: From Information Retrieval to Biological Discovery," Nature Rev. Genetics, vol.7, no.2, pp. 119-129, Feb. 2006.
5. A. Abi-Haidar, J. Kaur1, A. Maguitman, P. Radivojac, A. Retchsteiner, K. Verspoor, Z. Wang, and L.M. Rocha, "Uncovering Protein Interaction in Abstracts and Text Using a Novel Linear Model and Word Proximity Networks," Genome Biology, vol.9, suppl. 2: S11.1-19, 2008.
6. L. Hirschman, A. Yeh, C. Blaschke, and A. Valencia, "Overview of Biocreative: Critical Assessment of Information Extraction for Biology," BMC Bioinformatics, vol.6, suppl. 1: S1, 2005.
7. Proc. Second BioCreative Challenge Evaluation Workshop, 2007.
8. S. Chakrabarti, Mining the Web: Analysis of Hypertext and Semi Structured Data. Morgan Kaufmann, 2002.
9. I. Androutsopoulos, J. Koutsias, K.V. Chandrinos, and C.D. Spyropoulos, "An Experimental Comparison of Naive Bayesian and Keyword-Based Anti-Spam Filtering with Personal E-Mail Messages," Proc. Ann. ACM Conf. Research and Development in Information Retrieval, pp. 160-167, 2000.
10. T. Joachims, Learning to Classify Text Using Support Vector Machines: Methods, Theory, and Algorithms. Kluwer Academic Publishers, 2002.
11. R. Feldman and J. Sanger, The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge Univ. Press, 2006.
12. F. Sebastiani, "Machine Learning in Automated Text Categorization," ACM Computing Surveys vol.34, no.1, pp. 1-47, 2002.
13. M. Krallinger and A. Valencia, "Evaluating the Detection and Ranking of Protein Interaction Relevant Articles: The Biocreative Challenge Interaction Article Sub-Task (ias)," Proc. Second Biocreative Challenge Evaluation Workshop, pp. 29-39, 2007.
14. H.W. Mewes, C. Amid, R. Arnold, D. Frishman, U. Guldener, G. Mannhaupt, M. Munsterkotter, P. Pagel, N. Strack, V. Stumpflen, J. Warfsmann, and A. Ruepp, "Mips: Analysis Annotation of Proteins from Whole Genomes," Nucleic Acids Research, vol.32, Database issue, pp. D41-D44, Jan. 2004.
15. F. Fdez-Riverola, E. Iglesias, F. Diaz, J. Mendez, and J. Corchado, "Spamhunting: An Instance-Based Reasoning System for Spam Labelling Filtering," Decision Support Systems, vol.43, no.3, pp. 722-736, 2007.
16. G. Salton and C. Buckley, "Term-Weighting Approaches in Automatic Text Retrieval," Information Processing and Management, vol.24, no.5, pp. 513-523, 1988.
17. M. Porter, "An Algorithm for Suffix Stripping," Program, vol.13, no.3, pp. 130-137, 1980.
18. R. Breitling, P. Armengaud, A. Amtmann, and P. Herzyk, "Rank Products: A Simple yet Powerful and New Method to Detect Differentially Regulated Genes in Replicated Microarray Experiments," FEBS Letters, vol.573, nos. 1-3, pp. 83-92, Aug. 2004.
19. B. Settles, "Abner: An Open Source Tool for Automatically Tagging Genes, Proteins and Other Entity Names in Text," Bioinformatics, vol.21, no.14, pp. 3191-3192, 2005.
20. R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval. Addison-Wesley Longman, 1999.
21. P. Baldi, "Assessing the Accuracy of Prediction Algorithms for Classification: An Overview," Bioinformatics, vol.16, no.5, pp. 412-424, May 2000.
22. L.E. Dodd and M.S. Pepe, "Partial AUC Estimation Regression," Biometrics, vol.59, no.3, pp. 614-623, 2003.
23. T. Fawcett, "An Introduction to ROC Analysis," Pattern Recognition Letters, vol.27, no.8, pp. 861-874, 2006.
24. B.W. Matthews, "Comparison of the Predicted and Observed Secondary Structure of t4 Phage Lysozyme," Biochimica Biophysica Acta, vol.405, no.2, pp. 442-451, Oct. 1975.
25. T. Cover and J. Thomas, Elements of Information Theory. John Wiley and Sons, 2006.
26. I. Councill, C. Giles, and M. Kan, "Parscit: An Open-Source CRF Reference String Parsing Package," Proc. Int'l Conf. Language Resources and Evaluation (LREC), 2008.
27. U. Laemmli et al., "Cleavage of Structural Proteins During the Assembly of the Head of Bacteriophage t4," Nature, vol.227, no.5259, pp. 680-685, 1970.
28. D. Perkins et al., "Probability-Based Protein Identification by Searching Sequence Databases Using Mass Spectrometry Data," Electrophoresis, vol.20, no.18, pp. 3551-3567, 1999.
29. Y. Yang and J.O. Pedersen, "A Comparative Study on Feature Selection in Text Categorization," Proc. 14th Int'l Conf. Machine Learning, pp. 412-420, 1997.
30. T. Joachims, "Making Large-Scale Support Vector Machine Learning Practical," Advances in Kernel Methods: Support Vector Learning, MIT Press, 1999.
31. P. Nakov, A. Schwartz, and M. Hearst, "Citances: Citation Sentences for Semantic Analysis of Bioscience Text," Proc. SIGIR04 Workshop Search and Discovery in Bioinformatics, 2004.
32. K. Lai and S. Wu, "Using the Patent Co-Citation Approach to Establish a New Patent Classification System," Information Processing and Management, vol.41, no.2, pp. 313-330, 2005.
33. X. Li, H. Chen, Z. Zhang, and J. Li, "Automatic Patent Classification Using Citation Network Information: An Experimental Study in Nanotechnology," Proc. Seventh ACM/IEEE Computer Soc. Joint Conf. Digital Libraries, pp. 419-427, 2007.