PPIevo: Protein-protein interaction prediction from PSSM based evolutionary information

Genomics

Volumn 102, Issue 4, 2013, Pages 237-242

  Zahiri, Javad ^a   Yaghoubi, Omid ^b   Mohammad Noori, Morteza ^a   Ebrahimpour, Reza ^c   Masoudi Nejad, Ali ^a  

^a UNIVERSITY OF TEHRAN   (Iran)

^b Shamsipour Technical and Professional University   (Iran)

^c SHAHID RAJAEE TEACHER TRAINING UNIVERSITY   (Iran)

Author keywords

Computational intelligence; Machine learning; Position specific scoring matrix; Protein interaction networks; Protein protein interaction map

Indexed keywords

ACCURACY; AMINO ACID SEQUENCE; AREA UNDER THE CURVE; ARTICLE; CALCULATION; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; EVOLUTIONARY ALGORITHM; LEARNING ALGORITHM; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; POSITION WEIGHT MATRIX; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; RECEIVER OPERATING CHARACTERISTIC; SEQUENCE ANALYSIS; VALIDATION PROCESS;

COMPUTATIONAL INTELLIGENCE; MACHINE LEARNING; POSITION-SPECIFIC SCORING MATRIX; PROTEIN INTERACTION NETWORKS; PROTEIN-PROTEIN INTERACTION MAP;

ALGORITHMS; AMINO ACID SEQUENCE; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; EVOLUTION, MOLECULAR; HUMANS; PHYLOGENY; POSITION-SPECIFIC SCORING MATRICES; PROTEIN INTERACTION MAPS; PROTEINS;

EID: 84886313848     PISSN: 08887543     EISSN: 10898646     Source Type: Journal    
DOI: 10.1016/j.ygeno.2013.05.006     Document Type: Article

References (50)

1. Theofilatos K.A., Dimitrakopoulos C.M., Tsakalidis A.K., Likothanassis S.D., Papadimitriou S.T., Mavroudi S.P. Computational approaches for the prediction of protein-protein interactions: a survey. Curr. Bioinforma. 2011, 6:398-414.
2. Tuncbag N., Kar G., Keskin O., Gursoy A., Nussinov R. A survey of available tools and web servers for analysis of protein-protein interactions and interfaces. Brief. Bioinform. 2009, 10:217-232.
3. Shoemaker B., Panchenko A. Deciphering protein-protein interactions. Part II. Computational methods to predict protein and domain interaction partners. PLoS Comput. Biol. 2007, 3:595-601.
4. Lo S.L., Cai C.Z., Chen Y.Z., Chung M.C. Effect of training datasets on support vector machine prediction of protein-protein interactions. Proteomics 2005, 5:876-884.
5. Shen J., Zhang J., Luo X., Zhu W., Yu K., Chen K., Li Y., Jiang H. Predicting protein-protein interactions based only on sequences information. Proc. Natl. Acad. Sci. 2007, 104:4337-4341.
6. Narayanan E.K.a.A. Discovering gene networks with a neural-genetic hybrid. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2005, 231-242.
7. Lu L.J., Xia Y., Paccanaro A., Yu H., Gerstein M. Assessing the limits of genomic data integration for predicting protein networks. Genome Res. 2005, 15:945-953.
8. Chen X.W., Liu M. Prediction of protein-protein interactions using random decision forest framework. Bioinformatics 2005, 21:4394-4400.
9. Elefsinioti A., Saraç Ö.S., Hegele A., Plake C., Hubner N.C., Poser I., Sarov M., Hyman A., Mann M., Schroeder M. Large-scale de novo prediction of physical protein-protein association. Mol. Cell Proteomics 2011, 10.
10. Jothi R., Kann M.G., Przytycka T.M. Predicting protein-protein interaction by searching evolutionary tree automorphism space. Bioinformatics 2005, 21(Suppl. 1):i241-i250.
11. Enright A., Iliopoulos I., Kyrpides N., Ouzounis C. Protein interaction maps for complete genomes based on gene fusion events. Nature 1999, 402:86-90.
12. Ideker T., Ozier O., Schwikowski B., Siegel A. Discovering regulatory and signalling circuits in molecular interaction networks. ISMB(Supp of Bioinformatics) 2002, 233-240.
13. Zhang Q.C., Petrey D., Deng L., Qiang L., Shi Y., Thu C.A., Bisikirska B., Lefebvre C., Accili D., Hunter T. Structure-based prediction of protein-protein interactions on a genome-wide scale. Nature 2012, 490:556-560.
14. Liu G., Li J., Wong L. Assessing and Predicting Protein Interactions Using Both Local and Global Network Topological Metrics, Genome Informatics 2008.
15. Jaeger S., Gaudan S., Leser U., Rebholz-Schuhmann D. Integrating protein-protein interactions and text mining for protein function prediction. BMC Bioinforma. 2008, 9(Suppl. 8):S2.
16. Yu J., Guo M., Needham C., Huang Y., Cai L., Westhead D. Simple sequence-based kernels do not predict protein-protein interactions. Bioinformatics 2010, 26:2610-2614.
17. Keshava Prasad T.S., Goel R., Kandasamy K., Keerthikumar S., Kumar S., Mathivanan S., Telikicherla D., Raju R., Shafreen B., Venugopal A., Balakrishnan L., Marimuthu A., Banerjee S., Somanathan D.S., Sebastian A., Rani S., Ray S., Harrys Kishore C.J., Kanth S., Ahmed M., Kashyap M.K., Mohmood R., Ramachandra Y.L., Krishna V., Rahiman B.A., Mohan S., Ranganathan P., Ramabadran S., Chaerkady R., Pandey A. Human protein reference database-2009 update. Nucleic Acids Res. 2009, 37:D767-D772.
18. Ben-Hur A., Noble W. Kernel methods for predicting protein-protein interactions. Bioinformatics 2005, 21:i38-i46.
19. Zaki N., Deris S., Alashwal H. Protein-protein interaction detection based on substring sensitivity measure. Int. J. Biol. Med. Sci. 2006, 1.
20. Zhang L.V., Wong S.L., King O.D., Roth F.P. Predicting co-complexed protein pairs using genomic and proteomic data integration. BMC Bioinforma. 2004, 5:38.
21. Han D., Kim H.S., Seo J., Jang W. A domain combination based probabilistic framework for protein-protein interaction prediction. Genome Inform. Ser. 2003, 250-260.
22. Zhang Y.N., Pan X.Y., Huang Y., Shen H.B. Adaptive compressive learning for prediction of protein-protein interactions from primary sequence. J. Theor. Biol. 2011, 283:44-52.
23. Ezkurdia I., Bartoli L., Fariselli P., Casadio R., Valencia A., Tress M.L. Progress and challenges in predicting protein-protein interaction sites. Brief. Bioinform. 2009, 10:233-246.
24. Qi Y., Klein-Seetharaman J., Bar-Joseph Z. Random forest similarity for protein-protein interaction prediction from multiple sources. Pac. Symp. Biocomput. 2005, 531-542.
25. Wang J., Li C., Wang E., Wang X. Uncovering the rules for protein-protein interactions from yeast genomic data. Proc. Natl. Acad. Sci. 2009, 106:3752-3757.
26. Rhodes D.R., Tomlins S.A., Varambally S., Mahavisno V., Barrette T., Kalyana-Sundaram S., Ghosh D., Pandey A., Chinnaiyan A.M. Probabilistic model of the human protein-protein interaction network. Nat. Biotechnol. 2005, 23:951-959.
27. Jansen R., Yu H., Greenbaum D., Kluger Y., Krogan N.J., Chung S., Emili A., Snyder M., Greenblatt J.F., Gerstein M. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 2003, 302:449-453.
28. Lu L.J., Xia Y., Paccanaro A., Yu H., Gerstein M. Assessing the limits of genomic data integration for predicting protein networks. Genome Res. 2005, 15:945-953.
29. Braun P., Tasan M., Dreze M., Barrios-Rodiles M., Lemmens I., Yu H., Sahalie J.M., Murray R.R., Roncari L., De Smet A.S. An experimentally derived confidence score for binary protein-protein interactions. Nat. Methods 2008, 6:91-97.
30. Letunic I., Doerks T., Bork P. SMART 7: recent updates to the protein domain annotation resource. Nucleic Acids Res. 2012, 40:D302-D305.
31. Punta M., Coggill P.C., Eberhardt R.Y., Mistry J., Tate J., Boursnell C., Pang N., Forslund K., Ceric G., Clements J., Heger A., Holm L., Sonnhammer E.L., Eddy S.R., Bateman A., Finn R.D. The Pfam protein families database. Nucleic Acids Res. 2012, 40:29.
32. Chen X.W., Jeong J.C., Dermyer P. KUPS: constructing datasets of interacting and non-interacting protein pairs with associated attributions. Nucleic Acids Res. 2011, 39:D750-D754.
33. Ben-Hur A., Noble W.S. Choosing negative examples for the prediction of protein-protein interactions. BMC Bioinforma. 2006, 7:S2.
34. Shoyaib M., Abdullah-Al-Wadud M., Chae O. Selecting Negative Examples for Protein-Protein Interaction 2013, 2013 (in press).
35. Browne F., Wang H., Zheng H., Azuaje F. GRIP: a web-based system for constructing Gold Standard datasets for protein-protein interaction prediction. Source Code Biol. Med. 2009, 4:2.
36. Liu Z.-P., Chen L. Proteome-wide prediction of protein-protein interactions from high-throughput data. Protein Cell 2012, 3:508-520.
37. Stark C., Breitkreutz B.J., Chatr-Aryamontri A., Boucher L., Oughtred R., Livstone M.S., Nixon J., Van Auken K., Wang X., Shi X., Reguly T., Rust J.M., Winter A., Dolinski K., Tyers M. The BioGRID interaction database: 2011 update. Nucleic Acids Res. 2011, 39:D698-D704.
38. Kerrien S., Aranda B., Breuza L., Bridge A., Broackes-Carter F., Chen C., Duesbury M., Dumousseau M., Feuermann M., Hinz U., Jandrasits C., Jimenez R.C., Khadake J., Mahadevan U., Masson P., Pedruzzi I., Pfeiffenberger E., Porras P., Raghunath A., Roechert B., Orchard S., Hermjakob H. The IntAct molecular interaction database in 2012. Nucleic Acids Res. 2012, 40:D841-D846.
39. Licata L., Briganti L., Peluso D., Perfetto L., Iannuccelli M., Galeota E., Sacco F., Palma A., Nardozza A.P., Santonico E., Castagnoli L., Cesareni G. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res. 2012, 40:D857-D861.
40. Smialowski P., Pagel P., Wong P., Brauner B., Dunger I., Fobo G., Frishman G., Montrone C., Rattei T., Frishman D., Ruepp A. The Negatome database: a reference set of non-interacting protein pairs. Nucleic Acids Res. 2010, 38:D540-D544.
41. Ben-Hur A., Noble W.S. Choosing negative examples for the prediction of protein-protein interactions. BMC Bioinforma. 2006, 7(Suppl. 1):S2.
42. Xenarios I., Salwinski L., Duan X.J., Higney P., Kim S.M., 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.
43. Altschul S., Madden T., Schffer A., Zhang Z.J., Miller W., Lipman D. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997, 25:3389-3402.
44. Kantardzic M. Data Mining: Concepts, Models, Methods, and Algorithms 2011, Wiley-IEEE Press.
45. Hausser J., Strimmer K. Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks. J. Mach. Learn. Res. 2009, 10:1469-1484.
46. Geurts P., Irrthum A., Wehenkel L. Supervised learning with decision tree-based methods in computational and systems biology. Mol. Biosyst. 2009, 5:1593-1605.
47. Chen X., Wang M., Zhang H. The use of classification trees for bioinformatics. Wiley Interdiscip. Rev. Data Min. Knowl. Disc. 2011, 1:55-63.
48. Thahir M., Jaime C., Madhavi G. Active learning for human protein-protein interaction prediction. BMC Bioinforma. 2010, 11.
49. Li B.Q., Feng K.Y., Chen L., Huang T., Cai Y.D. Prediction of protein-protein interaction sites by random forest algorithm with mRMR and IFS. PLoS One 2012, 7:e43927.
50. Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I.H. The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 2009, 11:10-18.