CytoSVM: An advanced server for identification of cytokine-receptor interactions

Nucleic Acids Research

Volumn 35, Issue SUPPL.2, 2007, Pages

  Xu, Jin Rui ^a   Zhang, Jing Xian ^a   Han, Bu Cong ^a   Liang, Liang ^a   Ji, Zhi Liang ^a,b  

^a BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^b XIAMEN UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CYTOKINE; DIPEPTIDE; PROTEIN;

ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGY; CHEMISTRY; CLASSIFICATION; COMPUTER INTERFACE; COMPUTER PROGRAM; GENOME; HUMAN; INTERNET; METHODOLOGY; PROTEIN DATABASE; REPRODUCIBILITY; SEQUENCE ANALYSIS;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; CYTOKINES; DATABASES, PROTEIN; DIPEPTIDES; GENOME; HUMANS; INTERNET; PROTEINS; REPRODUCIBILITY OF RESULTS; SEQUENCE ANALYSIS, PROTEIN; SOFTWARE; USER-COMPUTER INTERFACE;

EID: 34547603387     PISSN: 03051048     EISSN: 13624962     Source Type: Journal    
DOI: 10.1093/nar/gkm254     Document Type: Article

References (25)

1. Oppenheim, J.J. (2001) Cytokines: past, present, and future. Int. J. Hematol., 74, 3-8.
2. Altermann, E. and Klaenhammer, T.R. (2005) PathwayVoyager: pathway mapping using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. BMC Genomics, 6, 60-66.
3. Pazos, F., Ranea, J.A., Juan, D. and Sternberg, M.J. (2005) Assessing protein co-evolution in the context of the tree of life assists in the prediction of the interactome. J. Mol. Biol., 352, 1002-1015.
4. Goh, C.S., Bogan, A.A., Joachimiak, M., Walther, D. and Cohen, F.E. (2000) Co-evolution of proteins with their interaction partners. J. Mol. Biol., 299, 283-293.
5. Fujiwara, Y. and Asogawa, M. (2002) Protein function prediction using hidden Markov models and neural networks. NEC. Res. Dev., 43, 238-241.
6. Di Gennaro, J.A., Siew, N., Hoffman, B.T., Zhang, L., Skolnick, J., Neilson, L.I. and Fetrow, J.S. (2001) Enhanced functional annotation of protein sequences via the use of structural descriptors. J. Struct. Biol., 134, 232-245.
7. Teichmann, S.A., Murzin, A.G. and Chothia, C. (2001) Determination of protein function, evolution and interactions by structural genomics. Curr Opin Struct Biol., 11, 354-363.
8. Chen, R. and Weng, Z. (2002) Docking unbound proteins using shape complementarity, desolvation, and electrostatics. Proteins, 47, 281-294.
9. Enright, A.J., Iliopoulos, I., Kyrpides, N.C. and Ouzounis, C.A. (1999) Protein interaction maps for complete genomes based on gene fusion events. Nature, 402, 86-90.
10. Marcotte, E.M., Pellegrini, M., Ng, H.L., Rice, D.W., Yeates, T.O. and Eisenberg, D. (1999) Detecting protein function and protein-protein interactions from genome sequences. Science, 285, 751-753.
11. Hodges, H.C. and Tsai, J.W. (2002) 3D-Motifs: an informatics approach to protein function prediction. FASEB. J., 16, A543-A543.
12. Enright, A.J., Van Dongen, S. and Ouzounis, C.A. (2002) an efficient algorithm for large-scale detection of protein families. Nucleic Acids Res., 30, 1575-1584.
13. Cai, C.Z., Han, L.Y., Ji, Z.L., Chen, X. and Chen, Y.Z. (2003) SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res., 31, 3692-3697.
14. Huang, N., Chen, H. and Sun, Z. (2005) CTKPred: an SVM-based method for the prediction and classification of the cytokine superfamily. Protein Eng. Des. Sel., 18, 365-368.
15. Karchin, R., Karplus, K. and Haussler, D. (2002) Classifying G-protein coupled receptors with support vector machines. Bioinformatics, 18, 147-159.
16. Ding, C.H. and Dubchak, I. (2001) Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics, 17, 349-358.
17. Yuan, Z., Burrage, K. and Mattick, J.S. (2002) Prediction of protein solvent accessibility using support vector machines. Proteins, 48, 566-570.
18. Hua, S. and Sun, Z. (2001) A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. J. Mol. Biol., 308, 397-407.
19. Bock, J.R. and Gough, D.A. (2001) Predicting protein\protein interactions from primary structure. Bioinformatics, 17, 455-460.
20. Bateman, A., Coin, L., Durbin, R., Finn, R.D., Hollich, V., Griffiths-Jones, S., Khanna, A., Marshall, M., Moxon, S. et al. (2004) The Pfam protein families database. Nucleic Acids Res., 32, D138-D141.
21. Burges, C. (1998) A Tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc., 2, 121-167.
22. Vapnik, V.N. (1995) The Nature of Statistical Learning Theory Springer, New York.
23. Platt, J.C. (2000) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Smola, A.J., Bartlett, P.L., Scholkopf, B. and Schuurnabs, D. (eds), Advances in Large Margin Classiers. MIT Press, Cambridge, MA, pp. 61-74.
24. Baldi, P., Brunak, S., Chauvin, Y., Andersen, C.A. and Nielsen, H. (2000) Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics, 16, 412-424.
25. Hanley, J.A. and McNeil, B.J. (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143, 29-36.