Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning

Bioinformatics

Volumn 26, Issue 6, 2010, Pages 807-813

  Wu, Chia Chin ^a   Asgharzadeh, Shahab ^b,c   Triche, Timothy J ^b,c   D'Argenio, David Z ^a  

^a University of Southern California   (United States)

^b UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^c UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; FACTUAL DATABASE; GENETICS; GENOMICS; HUMAN; METHODOLOGY; SIGNAL TRANSDUCTION;

DATABASES, FACTUAL; GENOMICS; HUMANS; SIGNAL TRANSDUCTION;

EID: 77951953607     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btq044     Document Type: Article

References (46)

1. Ashburner, M. et al. (2000) Gene Ontology: tool for the unification of biology. Nat. Genet., 25, 25-29.
2. Basso, K. et al. (2005) Reverse engineering of regulatory networks in human B cells Nat. Genet., 37, 382-390.
3. Ben-Hur, A. and Noble, W.S. (2005) Kernel methods for predicting protein-protein interactions. Bioinformatics, 21(Suppl 1), i38-i46.
4. Ben-Hur, A. and Noble, W.S. (2006) Choosing negative examples for the prediction of protein-protein interactions. BMC Bioinformatics, 7(Suppl 1), S2.
5. Bowd, C. et al. (2005) Relevance vector machine and support vector machine classifier analysis of scanning laser polarimetry retinal nerve fiber layer measurements, Invest. Ophthalmol. Vis. Sci., 46, 1322-1329.
6. Bowers, P.M. et al. (2004) Prolinks: a database of protein functional linkages derived from coevolution. Genome Biol., 5, R35.
7. Breiman, L. (2001) Random forests. Machine Learn., 45, 5-32.
8. Dijkstra, E.W. (1959) A note on two problems in connexion with graphs. Numerische Math., 1, 269-271.
9. Do, T.N. and Fekete, J.D. (2007) Large scale classification with support vector machine algorithms, Proc. Sixth Intl. Conf. Machine Learn. Appl., 7-12.
10. Down, T.A. and Hubbard, T.J. (2004) What can we learn from noncoding regions of similarity between genomes? BMC Bioinformatics, 5, 131.
11. Entrez Gene database. Available at http://www.ncbi.nlm.nih.gov/sites/entrez?db=gene (last accessed date December, 2008).
12. Ferretti, V. et al. (2007) PReMod: a database of genome-wide mammalian cis-regulatory module predictions. Nucleic Acids Res., 35(Database issue), D122-D126.
13. Franke, L. et al. (2006) Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. Am. J. Hum. Genet., 78, 1011-1025.
14. Freund, Y. and Schapire, R.E. (1997) Decision-theoretic generalization of on-line learning and an application to boosting. J. Comp. & Sys. Sci., 55, 119-139.
15. Gary, D. et al. (2003) BIND: the biomolecular interaction network database. Nucleic Acids Res., 31, 248-250.
16. Jansen, R. and Gerstein, M. (2004) Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction. Curr. Opin. Microbiol., 7, 535-545.
17. Jansen, R. et al. (2003) A Bayesian network approach for predicting protein-protein interactions from genomic data. Science, 302, 449-453.
18. Keshava Prasad, T.S. et al. (2009) Human protein reference database-2009 update. Nucleic Acids Res., 37, D767-D772.
19. Kondor, R.I. and Lafferty, J. (2002) Diffusion kernels on graphs and other discrete structures. Proc. 19th Intl. Conf. Machine Learn., 315-322.
20. Krishnapuram, B. et al. (2004) Joint classifier and feature optimization for comprehensive cancer diagnosis using gene expression data. J. Comput. Biol., 11, 227-242.
21. Lee, I. et al. (2004) A probabilistic functional network of yeast genes. Science, 306, 1555-1558.
22. Lee, T.I. et al. (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 298, 799-804.
23. Li, X. et al. (2005) AdaBoost with SVM-based component classifiers. Eng. Appl. Artificial Intell., 21, 785-795.
24. Linding, R. et al. (2008) NetworKIN: a resource for exploring cellular phosphorylation networks. Nucleic Acids Res., 36, D695-D699.
25. Loging, W. et al. (2007) High-throughput electronic biology: mining information for drug discovery. Nat. Rev. Drug Discov., 6, 220-230.
26. Ng, S.K. et al. (2003) InterDom: a database of putative interacting protein domains for validating predicted protein interactions and complexes. Nucleic Acids Res., 31, 251-254.
27. Obayashi, T. et al. (2008) COXPRESdb: a database of coexpressed gene networks in mammals. Nucleic Acids Res., 36, D77-D82.
28. Opitz, D.W. et al. (1999) Popular ensemble methods: an empirical study. J. Artificial Intell. Res., 11, 169-198.
29. Papin, J.A. et al. (2005) Reconstruction of cellular signaling networks and analysis of their properties. Nat. Rev. Mol. Cell Biol., 6, 99-111.
30. Pavlidis, P. et al. (2001) Gene functional classification from heterogeneous data. RECOMB, 249-255.
31. Polikar, R. (2006) Ensemble based systems in decision making. IEEE Circuits & Systems Mag., 6, 21-45.
32. Qi, Y. et al. (2006) Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins, 63, 490-500.
33. Qiu, J. and Noble, W.S. (2008) Predicting co-complexed protein pairs from heterogeneous data. PLoS Comput. Biol., 4, e1000054.
34. Rhodes, D.R. et al. (2005) Probabilistic model of the human protein-protein interaction network. Nat. Biotechnol., 23, 951-959.
35. Rual, J.F. et al. (2005) Towards a proteome-scale map of the human protein-protein interaction network. Nature, 437, 1173-1178.
36. Saar-Tsechansky, M. and Provost, F. (2007) Handling missing values when applying classification models. J. Machine Learn. Res., 8, 1625-1657.
37. Shannon, P. et al. (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res., 13, 2498-24504.
38. Stears, R.L. et al. (2003) Trends in microarray analysis. Nature Med., 9, 140-145.
39. Stoughton, R.B. and Friend, S.H. (2005) How molecular profiling could revolutionize drug discovery. Nat. Rev. Drug Discov., 4, 345-350.
40. Tipping, M.E. (2001) Sparse Bayesian learning and the Relevance Vector Machine. J. Machine Learn. Res., 1, 211-244.
41. Tipping, M.E. and Faul, A. (2003) Fast marginal likelihood maximization for sparse Bayesian models. Proc. Nineth Artificial Intell. & Stat., 3-6.
42. Troyanskaya, O.G. et al. (2003) A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Proc. Natl Acad. Sci. USA, 100, 8348-8353.
43. Vastrik, I. et al. (2007) Reactome: a knowledge base of biologic pathways and processes. Genome Biol., 8, R39.
44. Van Holsbeke, C. et al. (2007) External validation of mathematical models to distinguish between benign and malignant adnexal tumors: a multicenter study by the International Ovarian Tumor Analysis Group. Clin. Cancer Res., 13(15 Pt 1), 4440-447.
45. Yellaboina, S. et al. (2007) Inferring genome-wide functional linkages in E. coli by combining improved genome context methods: comparison with high-throughput experimental data. Genome Res., 17, 527-535.
46. Zhong, W. and Sternberg, P.W. (2006) Genome-wide prediction of C. elegans genetic interactions. Science, 311, 1481-1484.