The accurate prediction of protein family from amino acid sequence by measuring features of sequence fragments

Journal of Computational Biology

Volumn 16, Issue 12, 2009, Pages 1671-1688

  Hong, Huixiao ^a   Hong, Qilong ^b   Perkins, Roger ^a   Shi, Leming ^a   Fang, Hong ^a   Su, Zhenqiang ^a   Dragan, Yvonne ^a   Fuscoe, James C ^a   Tong, Weida ^a  

^a NATIONAL CENTER FOR TOXICOLOGICAL RESEARCH   (United States)

^b 3202 Bell Engineering Center   (United States)

Author keywords

Prediction; Protein family; Protein feature; Recognition; Sequence

Indexed keywords

PROTEIN;

ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; CHEMISTRY; CLASSIFICATION; METHODOLOGY; MULTIGENE FAMILY; PROTEIN DATABASE; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS; SEQUENCE HOMOLOGY; THEORETICAL MODEL;

ALGORITHMS; AMINO ACID SEQUENCE; DATABASES, PROTEIN; MODELS, THEORETICAL; MULTIGENE FAMILY; PROTEINS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN; SEQUENCE HOMOLOGY, AMINO ACID;

EID: 75149194967     PISSN: 10665277     EISSN: None     Source Type: Journal    
DOI: 10.1089/cmb.2008.0115     Document Type: Article

References (21)

1. Allwein, E.L., Schapire, R.E., and Singer, Y. 2001. Reducing multiclass to binary: a unifying approach for margin classifiers. J. Mach. Learn. Res. 1, 113-141.
2. Anfinsen, B.C. 1973. Principles that govern the folding of protein chains. Science 181, 223-230.
3. Baldi, P., Brunak, S., Chauvin, Y., et al. 2000. Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16, 412-424.
4. Baldi. P., Chauvin, Y., Hunkapiller, T., et al. 1994. Hidden Markov models of biological primary sequence information. Proc. Natl. Acad. Sci. USA 91, 1059-1063.
5. Chang, C.-C., and Lin, C.-J. 2001. LIBSVM: a library for support vector machines [Software]. Available at: www.csie.ntu.edu.tw/*cjlin/libsvm. Accessed June 15, 2009.
6. Cortes, C., and Vapnik, V. 1995. Support-vector networks. Mach. Learn. 20, 273-297.
7. Gunn, S.R., Brown, M., and Bossley, K.M. 1997. Network performance assessment for neurofuzzy data modeling, 313- 324. In: Liu, X., Cohen, P. and Berthold, M. eds. Advances in Intelligent Data Analysis. Reasoning about Data. Springer-Verlag, New York.
8. Henikoff, S., and Henikoff, J.G. 1992. Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. USA 89, 10915-10919.
9. Huang, T.-K., Weng, R.C., and Lin, C.-J. 2006. Generalized Bradley-Terry models and multi-class probability estimates. J. Mach. Learn. Res. 7, 85-115.
10. Karchin, R., Karplus, K., and Haussler, D. 2002. Classifying g-protein coupled receptors with support vector machines. Bioinformatics 18, 147-159.
11. Keerthi, S.S., and Lin, C.J. 2003. Asymptotic behaviours of support vector machines with gaussian kernel. Neural Comput. 15, 1667-1689.
12. Krogh, A., Brown, M., Mian, I., et al. 1994. Hidden Markov models in computational biology. J. Mol. Biol. 235, 1501-1531.
13. Lapinsh, M., Gutcaits, A., Prusis, P., et al. 2002. Classification of G-protein coupled receptors by alignment independent extraction of principal chemical properties of primary amino acid sequences. Protein Sci. 11, 795-805.
14. Lin, H.-T., Lin, C.-J., and Weng, R.C. 2003. A note on Platt's probabilistic outputs for support vector machines. Available at: www.csie.ntu.edu.tw/*cjlin/papers.html. Accessed June 15, 2009.
15. Needle, S.B., and Wunsch, C.D. 1970. A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48, 443-453.
16. Platt, J.C. 2000. Probabilities for SV machine, 61-74. In: Smola, A., Bartlett, P., Scholkopf, B., et al., eds. Advances in Large Margin Classifiers. The MIT Press, Cambridge, MA.
17. Sgourakis, N.G., Bagos, P.G., Papasaikas, P.K., et al. 2005. A method for the prediction of GPCRs coupling specificity to G-proteins using refined profile hidden Markov models. BMC Bioinform. 6, 104-115.
18. Vries, J.K., Munshi, R., Tobi, D., et al. 2004. A sequence alignment-independent method for protein classification. Appl. Bioinform. 3, 137-148.
19. Wu, C.H., Huang, H., Nikolskaya, A., et al. 2004a. The iProClass integrated database for protein functional analysis. Comput. Biol. Chem. 28, 87-96.
20. Wu, C.H., Nikolskaya, A., Huang, H., et al. 2004b. PIRSF: family classification system at the Protein Information Resource. Nucleic Acids Res. 32, D112-D114.
21. Wu, T.-F., Lin, C.-J., and Weng, R.C. 2004c. Probability estimates for multi-class classification by pairwise coupling. J. Mach. Learn. Res. 5,975-1005.