AdaBoost based multi-instance transfer learning for predicting proteome-wide interactions between salmonella and human proteins

PLoS ONE

Volumn 9, Issue 10, 2014, Pages

  Mei, Suyu ^a,b   Zhu, Hao ^b  

^a SHENYANG NORMAL UNIVERSITY   (China)

^b SOUTHERN MEDICAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ADABOOST BASED MULTI INSTANCE TRANSFER LEARNING; ARTICLE; CLUSTER ANALYSIS; CONTROLLED STUDY; DATA ANALYSIS; GENE ONTOLOGY; HOST PATHOGEN INTERACTION; HUMAN; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; NONHUMAN; NUCLEOTIDE SEQUENCE; PATHOGENESIS; PREDICTION; PROTEIN PROTEIN INTERACTION; PROTEOMICS; SALMONELLA TYPHIMURIUM; VALIDATION PROCESS; GENETICS; MICROBIOLOGY; PATHOGENICITY; PROTEIN DATABASE; SALMONELLA ENTERICA SEROVAR TYPHIMURIUM; SALMONELLOSIS;

SALMONELLA;

PROTEOME;

DATABASES, PROTEIN; GENE ONTOLOGY; HOST-PATHOGEN INTERACTIONS; HUMANS; PROTEIN INTERACTION MAPS; PROTEOME; SALMONELLA INFECTIONS; SALMONELLA TYPHIMURIUM;

EID: 84908149049     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0110488     Document Type: Article

References (38)

1. von Mering C, Krause R, Snel B, Cornell M, Oliver SG, et al. (2002) Comparative assessment of large-scale datasets of protein-protein interactions. Nature 417: 399-403.
2. Edwards A, Kus B, Jansen R, Greenbaum D, Greenblatt J, et al. (2002) Bridging structural biology and genomics: assessing protein interaction data with known complexes. Trends Genet 18: 529-536.
3. Wu X, Zhu L, Guo J, Zhang D, Lin K (2006) Prediction of yeast protein-protein interaction network: insights from the Gene Ontology and annotations. Nucleic Acids Res 34: 2137-2150.
4. DeBodt S, Proost S, Vandepoele K, Rouzé P, Peer Y, et al. (2009) Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression. BMC Genomics 10: 288.
5. Shen J, Zhang J, Luo X, Zhu W, Yu K, et al. (2007) Predicting protein-protein interactions based only on sequences information. Proc Natl Acad Sci USA 104: 4337-41.
6. Fu W, Sanders-Beer BE, Katz KS, Maglott DR, Pruitt KD, et al. (2009) Human immunodeficiency virus type 1, human protein interaction database at NCBI. Nucleic Acids Res (Database Issue) 37: D417-22.
7. Wuchty S (2011) Computational prediction of host-parasite protein interactions between P. falciparum and H. sapiens. PLoS ONE 6: e26960.
8. Schleker S, Sun J, Raghavan B, Srnec M, Müller N, et al. (2012) The current Salmonella-host interactome. Proteomics Clin Appl 6: 117-133.
9. Tastan O, Qi Y, Carbonell J, Klein-Seetharaman J (2009) Prediction of interactions between HIV- 1 and human proteins by information integration. In: Proceedings of the Pacific Symposium on Biocomputing (PSB-2009). pp 516-527.
10. Qi Y, Tastan O, Carbone J, Klein-Seetharaman, Weston J, et al. (2010) Semi-supervised multi-task learning for predicting interactions between HIV-1 and human proteins. Bioinformatics 26: i645-i652.
11. Dyer M, Muralib T, Sobrala B (2011) Supervised learning and prediction of physical interactions between human and HIV proteins. Infect Genet Evol 11: 917-923.
12. Doolittle J, Gomez S (2010) Structural similarity-based predictions of protein interactions between HIV-1 and Homo sapiens. Virology J 7: 82.
13. Mukhopadhyay A, Maulik U, Bandyopadhyay S (2012) A novel biclustering approach to association rule mining for predicting HIV-1-human protein interactions. PLoS One 7: e32289.
14. Dyer M, Muralib T, Sobrala B (2007) Computational prediction of hostpathogen protein-protein interactions. Bioinformatics 23: i159-i166.
15. Schleker S, Garcia-Garcia J, Klein-Seetharaman J, Oliva B (2012) Prediction and comparison of Salmonella-human and Salmonella-Arabidopsis interactomes. Chem Biodivers 9: 991-1018.
16. Kshirsagar M, Carbonell J, Judith K (2012) Techniques to cope with missing data in host-pathogen protein interaction prediction. Bioinformatics 28: i466-i472.
17. Kshirsagar M, Carbonell J, Judith K (2013) Multitask learning for host-pathogen protein interactions. Bioinformatics 29: i217-i226.
18. Srikanth CV, Regino ML, Hallstrom K, McCormick B (2011) Salmonella effector proteins and host-cell responses. Cell Mol Life Sci 68: 3687-3697.
19. Yu J, Guo M, Needham C, Huang Y, Cai L, et al. (2010) Simple sequence-based kernels do not predict protein-protein interactions. Bioinformatics 26: 2610-2614.
20. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55: 119-139.
21. Vezhnevets A, Vezhnevets V (2005) Modest AdaBoost - teaching AdaBoost to generalize better. Graphicon 12: 987-997.
22. Jansen R, 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.
23. Boeckmann B, Bairoch A, Apweiler R, Blatter MC, Estreicher A, et al. (2003). The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res 31: 365-370.
24. Bhavsar A, Brown N, Stoepe J, Wiermer M, Martin D, et al. (2013) The Salmonella type III effector SspH2 specifically exploits the NLR co-chaperone activity of SGT1 to subvert immunity. PLoS Pathog 9: e1003518.
25. Pan S, Yang Q (2010) A Survey on Transfer Learning. IEEE Trans Knowl Data Eng 22: 1345-1359.
26. Mei S,Wang F, Zhou S (2011) Gene ontology based transfer learning for protein subcellular localization. BMC Bioinformatics 12: 44.
27. Mei S (2012) Multi-label multi-kernel transfer learning for human protein subcellular localization. PLoS ONE 7: e37716.
28. Mei S (2013) Probability weighted ensemble transfer learning for predicting interactions between HIV-1 and human proteins. PLoS ONE 8: e79606.
29. Altschul S, Madden T, Schaffer A, Zhang J, Zhang Z, et al. (1997) Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25: 3389-3402.
30. Barrell D, Dimmer E, Huntley R, Binns D, O'Donovan C, et al. (2009) The GOA database in 2009-an integrated Gene Ontology Annotation resource. Nucleic Acids Res (Database Issue) 37: D396-D403.
31. Meir R, Ratsch G (2003) An introduction to boosting and leveraging. Lect Notes Artif Int 2600: 118-183.
32. Davis J, Goadrich M (2006) The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning.
33. Schlumberger MC, Hardt WD (2005) Triggered phagocytosis by Salmonella: bacterial molecular mimicry of RhoGTPase activation/deactivation. Curr Top Microbiol Immunol 291: 29-42.
34. Wasylnka JA, Bakowski MA, Szeto J, Ohlson MB, Trimble WS, et al. (2008) Role for myosin II in regulating positioning of Salmonella-containing vacuoles and intracellular replication. Infect Immun 76: 2722-2735.
35. Vapnik V (1999) An Overview of Statistical Learning Theory. IEEE Trans Neural Netw 10: 988-999.
36. Mei S (2014) SVM ensemble based transfer learning for large-scale membrane proteins discrimination. J Theor Biol 340: 105-110.
37. Dong J, Adam K, Ching Y (2005) Fast SVM Training Algorithm with Decomposition on Very Large Data Sets. IEEE Trans Pattern Anal Mach Intell 27: 603-18.
38. Gonzalez MW, Kann MG (2012) Chapter 4: Protein Interactions and Disease. PLoS Comput Biol 8: e1002819.