Inference of functional relations in predicted protein networks with a machine learning approach

PLoS ONE

Volumn 5, Issue 4, 2010, Pages

  García Jiménez, Beatriz ^a   Juan, David ^b   Ezkurdia, Iakes ^b   Andrés León, Eduardo ^b   Valencia, Alfonso ^b  

^a UNIVERSIDAD CARLOS III DE MADRID   (Spain)

^b Structural Biology and Biocomputing Programme   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

BACTERIAL PROTEIN;

ACCURACY; ARTICLE; AVERAGED ONE DEPENDENCE ESTIMATOR BASED PREDICTOR OF PROTEIN INTERACTION; BAYESIAN LEARNING; CLASSIFIER; COMPUTER PREDICTION; CONTROLLED STUDY; ESCHERICHIA COLI; FUNCTIONAL PROTEOMICS; HIGH THROUGHPUT SCREENING; INTERMETHOD COMPARISON; LEARNING ALGORITHM; PROCESS DEVELOPMENT; PROCESS OPTIMIZATION; PROTEIN DATABASE; PROTEIN FUNCTION; PROTEIN PROTEIN INTERACTION; RECEIVER OPERATING CHARACTERISTIC; RELIABILITY; SEQUENCE ANALYSIS; TOTAL QUALITY MANAGEMENT; ALGORITHM; ARTIFICIAL INTELLIGENCE; BIOLOGY; CHEMICAL STRUCTURE; INFORMATION PROCESSING; INTERNET; METHODOLOGY; PROTEIN BINDING; PROTEOMICS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DATA COLLECTION; INTERNET; MODELS, MOLECULAR; PROTEIN BINDING; PROTEOMICS;

EID: 77956368930     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0009969     Document Type: Article

References (42)

1. Mering Cv, Krause R, Snel B, Cornell M, Oliver SG, et al. (2002) Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417(6887): 399-403.
2. Mering Cv, Huynen M, Jaeggi D, Schmidt S, Bork P, et al. (2003) STRING: A database of predicted functional associations between proteins. Nucleic Acids Res 31(1): 258-261.
3. Pellegrini M, Marcotte E, Thompson M, Eisenberg D, Yeates T (1999) Assigning protein functions by comparative genome analysis: Protein phylogenetic profiles. PNAS 96(8): 4285-4288.
4. Dandekar T, Snel B, Huynen M, Bork P (1998) Conservation of gene order: A fingerprint of proteins that physically interact. Trends in Biochemical Sciences 23(9): 324-328.
5. Enright A, Iliopoulos I, Kyrpides N, Ouzounis CA (1999) Protein interaction maps for complete genomes based on gene fusion events. Nature 402(6757): 86-90.
6. Marcotte EM, Pellegrini M, Ng HL, Rice DW, Yeates TO, et al. (1999) Detecting protein function and protein-protein interactions from genome sequences. Science 285(5428): 751-753.
7. Bowers P, Cokus S, Eisenberg D, Yeates T (2004) Use of logic relationships to decipher protein network organization. Science 306(5705): 2246-2249.
8. Morett E, Korbel J, Rajan E, SaabRincon G, Olvera L, et al. (2003) Systematic discovery of analogous enzymes in thiamin biosynthesis. Nat Biotechnol 21(7): 790-795.
9. Wu J, Kasif S, DeLisi C (2003) Identification of functional links between genes using phylogenetic profiles. Bioinformatics 19(12): 1524-1530.
10. Pazos F, Valencia A (2002) In silico two-hybrid system for the selection of physically interacting protein pairs. Proteins 47(2): 219-227.
11. Pazos F, Valencia A (2001) Similarity of phylogenetic trees as indicator of protein-protein interaction. Protein Eng 14(9): 609-614.
12. Juan D, Pazos F, Valencia A (2008) High-confidence prediction of global interactomes based on genome-wide coevolutionary networks. PNAS 105(3): 934-939.
13. Pazos F, Ranea JAG, Juan D, Sternberg MJE (2005) Assessing protein coevolution in the context of the tree of life assists in the prediction of the interactome. J Mol Biol 352(4): 1002-1015.
14. Sato T, Yamanishi Y, Kanehisa M, Toh H (2005) The inference of proteinprotein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships. Bioinformatics 21(17): 3482-3489.
15. Qi Y, Bar-Joseph Z, Klein-Seetharaman J (2006) Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins: Structure, Function, and Bioinformatics 63(3): 490-500.
16. Lu LJ, Xia Y, Paccanaro A, Yu H, Gerstein M (2005) Assessing the limits of genomic data integration for predicting protein networks. Genome Res 15(7): 945-953.
17. Webb GI, Boughton JR, Wang Z (2005) Not so naive bayes: Aggregating onedependence estimators. Mach Learn 58(1): 5-24.
18. Butland G, Peregrin-Alvarez JM, Li J, Yang W, Yang X, et al. (2005) Interaction network containing conserved and essential protein complexes in Escherichia coli. Nature 433(7025): 531-537.
19. Drummond C, Holte RC (2006) Cost curves: An improved method for visualizing classifier performance. Mach Learn 65(1): 95-130.
20. Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learning 29(2-3): 131-163.
21. Bouckaert RR Bayesian network classifiers in weka. Available: http://weka.sourceforge.net/manuals/weka.bn.pdf.
22. John GH, Langley P (1995) Estimating continuous distributions in Bayesian classifiers. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence. pp 338-345.
23. Breiman L (2001) Random forests. Mach Learn 45(1): 5-32.
24. Arifuzzaman M, Maeda M, Itoh A, Nishikata K, Takita C, et al. (2006) Largescale identification of protein-protein interaction of escherichia coli K-12. Genome Res 16(5): 686-691.
25. León EA, Ezkurdia I, García B, Valencia A, Juan D (2009) EcID. A database for the inference of functional interactions in E. coli. Nucl Acids Res 37(suppl_1): D629-635.
26. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215(3): 403-410.
27. Edgar RC (2004) MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32(5): 1792-1797.
28. Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, et al. (2006) From genomics to chemical genomics: New developments in KEGG. Nucleic Acids Res 34(Database issue): 354-7.
29. Keseler IM, Collado-Vides J, Gama-Castro S, Ingraham J, Paley S, et al. (2005) EcoCyc: A comprehensive database resource for escherichia coli. Nucleic Acids Res 33(Database issue): 334-7.
30. Hoffmann R, Valencia A (2004) A gene network for navigating the literature. Nat Genet 36(7): 664.
31. Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, et al. (2004) The database of interacting proteins: 2004 update. Nucleic Acids Res 32(Database issue): 449-51.
32. Alfarano C, Andrade CE, Anthony K, Bahroos N, Bajec M, et al. (2005) The biomolecular interaction network database and related tools 2005 update. Nucl Acids Res 33(suppl_1): 418-424.
33. Hermjakob H, Montecchi-Palazzi L, Lewington C, Mudali S, Kerrien S, et al. (2004) IntAct: An open source molecular interaction database. Nucleic Acids Res 32(Database issue): 452-5.
34. Sahami M (1996) Learning limited dependence bayesian classifiers. Proceedings of the Second International Conference on Knowledge Discovery in Databases and Data Mining (AAAI Press). pp 335-338.
35. Valencia A, Pazos F (2002) Computational methods for the prediction of protein interactions. Curr Opin Struct Biol 12(3): 368-373.
36. Freund Y, Mason L (1999) The alternating decision tree learning algorithm. Proceedings of the Sixteenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc, San Francisco, CA, USA. pp 124-133.
37. Cleary JG, Trigg LE (1995) K: An instance-based learner using an entropic distance measure. Proceedings of the Twelfth International Conference on Machine Learning, Morgan Kaufmann. pp 108-114.
38. Bishop CM (1995) Neural networks for pattern recognition. New York: Oxford University Press. 504 p.
39. Rumelhart DE, McClelland JL (1986) Parallel distributed processing. CambridgeMA: MIT Press. 567 p.
40. Frank E, Witten IH (1998) Generating accurate rule sets without global optimization. Proceedings of the Fifteenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc, San Francisco, CA, USA. Pp 144-151.
41. Cooper GF, Herskovits E (1992) A bayesian method for the induction of probabilistic networks from data. Mach Learn 9(4): 309-347.
42. Witten IH, Frank E (2005) Data mining: Practical machine learning tools and techniques. San Francisco: Morgan Kaufmann. 525 p.