Evaluation of different biological data and computational classification methods for use in protein interaction prediction

Proteins: Structure, Function and Genetics

Volumn 63, Issue 3, 2006, Pages 490-500

  Qi, Yanjun ^a   Bar Joseph, Ziv ^a   Klein Seetharaman, Judith ^a,b  

^a Carnegie Mellon University   (United States)

^b UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE   (United States)

Author keywords

High throughput data; Joint learning; Protein protein interaction

Indexed keywords

ACCURACY; ANALYTIC METHOD; ARTICLE; CLASSIFICATION; DATA ANALYSIS; GENE EXPRESSION; INFORMATION PROCESSING; PREDICTION; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; YEAST;

COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; FORECASTING; PROTEIN INTERACTION MAPPING;

EID: 33646018046     PISSN: 08873585     EISSN: 10970134     Source Type: Journal    
DOI: 10.1002/prot.20865     Document Type: Article

References (35)

1. Bader GD, Hogue CW. Analyzing yeast protein-protein interaction data obtained from different sources. Nat Biotechnol 2003;20:991-997.
2. von Mering C, Krause R, Snel B, Cornell M, Oliver S, Fields S, Bork P. Comparative assessment of large-scale data sets of protein-protein interactions. Nature 2002;417:399-403.
3. Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci USA 2001;10:4569-4574.
4. Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR, Lockshon D, Narayan V, Srinivasan M, Pochart P, Qureshi-Emili A, Li Y, Godwin B, Conover D, Kalbfleisch T, Vijayadamodar G, Yang M, Johnston M, Fields S, Rothberg JM. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 2000;403:623-627.
5. Gavin AC, Bosche M, Krause R, Grandi P, Marzioch M, Bauer A, Schultz J, Rick JM, Michon AM, Cruciat CM, Remor M, Hofert C, Schelder M, Brajenovic M, Ruffner H, Merino A, Klein K, Hudak M, Dickson D, Rudi T, Gnau V, Bauch A, Bastuck S, Huhse B, Leutwein C, Heurtier MA, Copley RR, Edelmann A, Querfurth E, Rybin V, Drewes G, Raida M, Bouwmeester T, Bork P, Seraphin B, Kuster B, Neubauer G, Superti-Furga G. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 2002;415:141-147.
6. Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L, Adams SL, Millar A, Taylor P, Bennett K, Boutilier K, Yang L, Wolting C, Donaldson I, Schandorff S, Shewnarane J, Vo M, Taggart J, Goudreault M, Muskat B, Alfarano C, Dewar D, Lin Z, Michalickova K, Willems AR, Sassi H, Nielsen PA, Rasmussen KJ, Andersen JR, Johansen LE, Hansen LH, Jespersen H, Podtelejnikov A, Nielsen E, Crawford J, Poulsen V, Sorensen BD, Matthiesen J, Hendrickson RC, Gleeson F, Pawson T, Moran MF, Durocher D, Mann M, Hogue CW, Figeys D, Tyers M. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 2002;415:6868.
7. Bar-Joseph Z, Gerber G, Lee T, Rinaldi NJ, Yoo JY, Robert F, Gordon DB, Fraenkel E, Jaakkola TS, Young RA, Gifford D. Computational discovery of gene modules and regulatory networks. Nat Biotechnol 2003;21:1337-1342.
8. Deng M, Mehta S, Sun F, Chen T. Inferring domain-domain interactions from protein-protein interactions. Genome Res 2002;12:1540-1548.
9. Bader JS, Chaudhuri A, Rothberg JM, Chant J. Gaining confidence in high-throughput protein interaction networks. Nat Biotechnol 2004;22:78-85.
10. Gilchrist MA, Salter LA, Wagner A. A statistical framework for combining and interpreting proteomic datasets. Bioinformatics 2004;20:689-700.
11. Jansen R, Yu H, Greenbaum D, Kluger Y, Krogan NJ, Chung S, Emili A, Snyder M, Greenblatt JF, Gerstein M. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 2003;302:449-453.
12. Lee I, Date SV, Adai AT, Marcotte EM. A probabilistic functional network of yeast genes. Science 2004;306:1555-1558.
13. Lin N, Wu B, Jansen R, Gerstein M, Zhao H. Information assessment on predicting protein-protein interactions. BMC Bioinformatics 2004;5:154.
14. Yamanishi Y, Vert JP, Kanehisa M. Protein network inference from multiple genomic data: a supervised approach. Bioinformatics 2004;20:363-370.
15. Zhang L, Wong S, King OD, Roth FP. Predicting co-complexed protein pairs using genomic and proteomic data integration. BMC Bioinformatics 2004;5:38.
16. Xenarios I, Salwinski L, Duan XJ, Higney P, Kim SM, Eisenberg D. DIP, the database of interacting proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res 2002;30:303-305.
17. Qi Y, Klein-Seetharaman J, Bar-Joseph Z. Random forest similarity for protein-protein interaction prediction from multiple sources. Pacific Symp Biocomput 2005;10:531-542.
18. Salwinski L, Eisenberg D. Computational methods of analysis ofprotein-protein interactions. Curr Opin Struct Biol 2003;13:377-382.
19. Sprinzak E, Sattath S, Margalit H. How reliable are experimental protein-protein interaction data. J Mol Biol 2003;327:919-923.
20. Mewes HW, Amid C, Arnold R, Frishman D, Guldener U, Mannhaupt G, Munsterkotter M, Pagel P, Strack N, Stumpften V, Warfsmann J, Ruepp A. MIPS: analysis and annotation of proteins from whole genomes. Nucleic Acids Res 2004;32(Database issue):D41-44.
21. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000;28:27-30.
22. Tong AH, Lesage G, Bader GD, Ding H, Xu H, Xin X, Young J, Berriz GF, Brost RL, Chang M, Chen Y, Cheng X, Chua G, Friesen H, Goldberg DS, Haynes J, Humphries C, He G, Hussein S, Ke L, Krogan N, Li Z, Levinson JN, Lu H, Menard P, Munyana C, Parsons AB, Ryan O, Tonikian R, Roberts T, Sdicu AM, Shapiro J, Sheikh B, Suter B, Wong SL, Zhang LV, Zhu H, Burd CG, Munro S, Sander C, Rine J, Greenblatt J, Peter M, Bretscher A, Bell G, Roth FP, Brown GW, Andrews B, Bussey H, Boone C. Global mapping of the yeast genetic interaction network. Science 2004;303:808-813.
23. The Gene Ontology Consortium. Gene ontology: tool for the unification of biology. Nat Genet 2000;25:25-29.
24. Joachims T. Learning to classify text using support vector machines. Dissertation. New York: Kluwer; 2002.
25. George HJ, Langley P. Estimating continuous distributions in bayesian classifiers. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence; 1995. pp 338-345.
26. Witten IH, Frank E, Data mining: practical machine learning tools with Java implementations. San Francisco, CA: Morgan Kaufmann; 2000.
27. Quinlan R. C4.5: programs for machine learning. San Francisco, CA: Morgan Kaufmann; 1993.
28. Breiman L. Random forests. Machine Learn 2001;45:5-32.
29. Jones KS. Information retrieval experiment. London: Butterworths; 1981. p 213-255.
30. Flach P. The many faces of ROC analysis in machine learning, ICML-04 Tutorial; 2004. Notes available from http://www. cs.bris.ac.uk/flach/ ICML04tutorial/.
31. Guyon I, Elisseeff A. An introduction to variable and feature selection. J Machine Learn Res 2003;5:1157-1182.
32. Ghaemmaghami S, Huh WK, Bower K, Howson RW, Belle A, Dephoure N, O'Shea EK, Weissman JS. Global analysis of protein expression in yeast. Nature 2003;425:737-741.
33. The Saccharomyces Genome Deletion Project: http://www-sequence. stanford.edu/group/yeast_deletion_project;, 2004.
34. Dolinski K, Balakrishnan R, Christie KR, Costanzo MC, et al. Saccharomyces Genome Database (SGD). http://www.yeastgenome. org; 2004.
35. Harbison CT, Gordon DB, Lee TI, Rinaldi NJ, Macisaac KD, Danford TW, Hannett NM, Tagne JB, Reynolds DB, Yoo J, Jennings EG, Zeitlinger J, Pokholok DK, Kellis M, Rolfe PA, Takusagawa KT, Lander ES, Gifford DK, Fraenkel E, Young RA. Transcriptional regulatory code of a eukaryotic genome. Nature 2004;431:99-104.