An empirical study of features fusion techniques for protein-protein interaction prediction

Current Bioinformatics

Volumn 11, Issue 1, 2016, Pages 4-12

  Zeng, Jiancang ^a   Li, Dapeng ^b   Wu, Yunfeng ^a   Zou, Quan ^c   Liu, Xiangrong ^a  

^a XIAMEN UNIVERSITY   (China)

^b Department of Radiology   (China)

^c TIANJIN UNIVERSITY   (China)

Author keywords

Features fusion; Features selection; Protein protein interaction; Random forests

Indexed keywords

FEATURE SELECTION; FORECASTING;

DATA SET; EMPIRICAL STUDIES; FEATURES FUSIONS; FEATURES SELECTION; FUSION TECHNIQUES; INTERACTION PREDICTION; N-GRAMS; PROTEIN FUNCTIONS; PROTEIN-PROTEIN INTERACTIONS; RANDOM FORESTS;

PROTEINS;

AMINO ACID SEQUENCE; ARTICLE; BIOINFORMATICS; CELLULAR DISTRIBUTION; CLASSIFIER; DECISION TREE; EMPIRICISM; FEATURE FUSION METHOD; HUMAN; NONHUMAN; PREDICTION; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN DATABASE; PROTEIN PROTEIN INTERACTION; RELIABILITY;

EID: 84961970485     PISSN: 15748936     EISSN: 2212392X     Source Type: Journal    
DOI: 10.2174/1574893611666151119221435     Document Type: Article

References (40)

1. Espadaler J, Querol E, Aviles FX, Oliva B. Identification of function-associated loop motifs and application to protein function prediction. Bioinformatics 2006; 22(18): 2237-43.
2. Jiang M, Chen Y, Zhang Y, et al. Identification of hepatocellular carcinoma related genes with k-th shortest paths in a proteinprotein interaction network. Mol BioSyst 2013; 9(11): 2720-8.
3. Wu X, Zhu L, Guo J, Zhang D-Y, Lin K. Prediction of yeast protein-protein interaction network: insights from the Gene Ontology and annotations. Nucleic Acids Res 2006; 34(7): 2137-50.
4. Sachs K, Perez O, Pe'er D, Lauffenburger DA, Nolan GP. Science 2005; 308: 523-29.
5. Plavec I, Sirenko O, Privat S, et al. Proc Natl Acad Sci USA 2004; 101: 1223-8.
6. Pawson T. Cell 2004; 116: 191-203.
7. Hu L, Huang T, Liu X-J, Cai Y-D. Predicting protein phenotypes based on protein-protein interaction network. PloS one 2011; 6(3): e17668.
8. Von Mering C, Krause R, Snel B, et al. Comparative assessment of large-scale data sets of protein-protein interactions. Nature 2002; 417(6887): 399-403.
9. Hawkins T, Luban S, Kihara D. Enhanced automated function prediction using distantly related sequences and contextual association by PFP. Protein Science 2006; 15(6): 1550-6.
10. Shen J, Zhang J, Luo X, et al. Predicting protein-protein interactions based only on sequences information. Proc Natl Acad Sci U S A 2007; 104(11): 4337-41.
11. Guo Y, Yu L, Wen Z, Li M. Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences. Nucleic Acids Res 2008; 36(9): 3025-30.
12. Liu L, Cai Y, Lu W, Feng K, Peng C, Niu B. Prediction of proteinprotein interactions based on PseAA composition and hybrid feature selection. Biochem Biophys Res Commun 2009; 380(2): 318-22.
13. Wang Y, Wang J, Yang Z, Deng N. Sequence-based proteinprotein interaction prediction via support vector machine. Journal of Systems Science and Complexity. 2010; 23(5): 1012-23.
14. Huang T, Wan S, Xu Z, et al. Analysis and prediction of translation rate based on sequence and functional features of the mRNA. PLos one 2011; 6(1): e16036.
15. Cai Y, Huang T, Hu L, Shi X, Xie L, Li Y. Prediction of lysine ubiquitination with mRMR feature selection and analysis. Amino acids 2012; 42(4): 1387-95.
16. Liu B, Xu J, Zou Q, Xu R, Wang X, Chen Q. Using distances between Top-n-gram and residue pairs for protein remote homology detection. BMC bioinformatics 2014; 15(Suppl 2): S3.
17. Liu B, Wang X, Lin L, Dong Q, Wang X. A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis. BMC bioinformatics 2008; 9(1): 510.
18. Lin C, Zou Y, Qin J, et al. Hierarchical classification of protein folds using a novel ensemble classifier. PloS one 2013; 8(2): e56499.
19. Zou Q, Li X, Jiang Y, Zhao Y, Wang G. BinMemPredict: a web server and software for predicting membrane protein types. Current Proteomics. 2013; 10(1): 2-9.
20. Pagel P, Kovac S, Oesterheld M, et al. The MIPS mammalian protein-protein interaction database. Bioinformatics 2005; 21(6): 832-4.
21. Schaefer CF, Anthony K, Krupa S, et al. PID: the pathway interaction database. Nucleic Acids Res 2009; 37(suppl 1): D674-D9.
22. Stark C, Breitkreutz B-J, Chatr-Aryamontri A, et al. The BioGRID interaction database: 2011 update. Nucleic acids research. 2011; 39(suppl 1): D698-D704.
23. Szklarczyk D, Franceschini A, Kuhn M, et al. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res 2011; 39(suppl 1): D561-D8.
24. Prasad TK, Goel R, Kandasamy K, et al. Human protein reference database-2009 update. Nucleic Acids Res 2009; 37(suppl 1): D767-D72.
25. Murali T, Pacifico S, Yu J, Guest S, Roberts GG, Finley RL. DroID 2011: a comprehensive, integrated resource for protein, transcription factor, RNA and gene interactions for Drosophila. Nucleic Acids Res 2011; 39(suppl 1): D736-D43.
26. Xenarios I, Salwinski L, Duan XJ, 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(1): 303-5.
27. Costanzo MC, Crawford ME, Hirschman JE, et al. YPD, PombePD and WormPD: model organism volumes of the BioKnowledge Library, an integrated resource for protein information. Nucleic Acids Res 2001; 29(1): 75-9.
28. Shen J, Zhang J, Luo X, et al. Predicting protein-protein interactions based only on sequences information. Proc Natl Acad Sci U S A 2007; 104(11): 4337-41.
29. Blohm P, Frishman G, Smialowski P, et al. Negatome 2.0: a database of non-interacting proteins derived by literature mining, manual annotation and protein structure analysis. Nucleic Acids Res 2013; gkt1079.
30. Huang Y, Li Y. Prediction of protein subcellular locations using fuzzy k-NN method. Bioinformatics 2004; 20(1): 21-8.
31. Yu CS, Lin CJ, Hwang JK. Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions. Protein Sci 2004; 13(5): 1402-6.
32. Leslie CS, Eskin E, Noble WS, editors. The spectrum kernel: A string kernel for SVM protein classification. Pac Symp Biocomput 2002.
33. Martin S, Roe D, Faulon J-L. Predicting protein-protein interactions using signature products. Bioinformatics 2005; 21(2): 218-26.
34. Dong Q, Zhou S, Liu X. Prediction of protein-protein interactions from primary sequences. Int J Data Min Bioinform 2010; 4(2): 211-27.
35. Yang S, Zheng F, Luo X, et al. Effective Dysphonia Detection Using Feature Dimension Reduction and Kernel Density Estimation for Patients with Parkinson's Disease. PloS one 2014; 9(2): e88825.
36. Ding C, Peng H. Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 2005; 3(02): 185-205.
37. Peng H, Long F, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and minredundancy. IEEE Trans Pattern Anal Mach Intell 2005; 27(8): 1226-38.
38. Breiman L. Random forests. Machine learning. 2001; 45(1): 5-32.
39. Zou Q, Zhao T, Liu Y, Guo M. Predicting RNA secondary structure based on the class information and Hopfield network. Comput Biol Med 2009; 39(3): 206-14.
40. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: an update. ACM SIGKDD explorations newsletter. 2009; 11(1): 10-8.