Context-specific independence mixture modelling for protein families

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4702 LNAI, Issue , 2007, Pages 79-90

  Georgi, Benjamin ^a   Schultz, Jörg ^b   Schliep, Alexander ^a  

^a MAX PLANCK INSTITUTE FOR MOLECULAR GENETICS   (Germany)

^b UNIVERSITY OF WÜRZBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BIOACTIVITY; CLUSTERING ALGORITHMS;

CONTEXT-SPECIFIC INDEPENDENCE; PYTHON MIXTURE PACKAGE;

PROTEINS;

EID: 38149028425     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74976-9_11     Document Type: Conference Paper

References (27)

1. Barash, Y., Friedman, N.: Context-specific bayesian clustering for gene expression data. J. Comput. Biol. 9(2), 169-191 (2002)
2. Biernacki, C., Celeux, G., Govaert, G.: An improvement of the NEC criterion for assessing the number of clusters in a mixture model. Non-Linear Anal. 20(3), 267-272 (1999)
3. Boutilier, C., Friedman, N., Goldszmidt, M., Koller, D.: Context-specific independence in Bayesian networks. In: Uncertainty in Artificial Intelligence, pp. 115-123 (1996)
4. Chakrabarti, S., Lanczycki, C.J.: Analysis and prediction of functionally important sites in proteins. Protein Sci. 16(1), 4-13 (2007)
5. Chickering, D.M., Heckerman, D.: Efficient approximations for the marginal likelihood of bayesian networks with hidden variables. Mach. Learn. 29(2-3), 181-212 (1997)
6. Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 1-38 (1977)
7. Friedman, N., Goldszmidt, M.: Learning bayesian networks with local structure. In: Proceedings of the NATO Advanced Study Institute on Learning in graphical models, pp. 421-459. Kluwer Academic Publishers, Norwell, MA, USA (1998)
8. Gasteiger, E., Gattiker, A., Hoogland, C., Ivanyi, I., Appel, D., Bairoch, A.: ExPASy: The proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. 31(13), 3784-3788 (2003)
9. Georgi, B., Schliep, A.: Context-specific independence mixture modeling for positional weight matrices. Bioinformatics 22(14), e166-173 (2006)
10. Georgi, B., Spence, M.A., Flodman, P., Schliep, A.: Mixture model based group inference in fused genotype and phenotype data. In: Studies in Classification, Data Analysis, and Knowledge Organization, Springer, Heidelberg (2007)
11. Hanks, S.K., Quinn, A.M., Hunter, T.: The protein kinase family: conserved features and deduced phylogeny of the catalytic domains. Science 241(4861), 42-52 (1988)
12. Hannenhalli, S., Russell, R.B.: Analysis and prediction of functional sub-types from protein sequence alignments. J. Mol. Biol. 303(1), 61-76 (2000)
13. Hunter, T.: Protein kinase classification. Methods Enzymol 200, 3-37 (1991)
14. Jones, S., Thornton, J.M.: Searching for functional sites in protein structures. Curr. Opin. Chem. Biol. 8(1), 3-7 (2004)
15. Lazareva-Ulitsky, B., Diemer, K., Thomas, P.D.: On the quality of tree-based protein classification. Bioinformatics 21(9), 1876-1890 (2005) Comparative Study
16. Lichtarge, O., Bourne, H.R., Cohen, F.E.: An evolutionary trace method defines binding surfaces common to protein families. J. Mol. Biol. 257(2), 342-358 (1996)
17. Liu, Y., Ruoho, A.E., Rao, V.D., Hurley, J.H.: Catalytic mechanism of the adenylyl and guanylyl cyclases: modeling and mutational analysis. Proc. Natl. Acad. Sci. USA 94(25), 13414-13419 (1997)
18. Livingstone, C.D., Barton, G.J.: Protein sequence alignments: a strategy for the hierarchical analysis of residue conservation. Comput. Appl. Biosci. 9(6), 745-756 (1993)
19. Manning, G., Whyte, D.B., Martinez, R., Hunter, T., Sudarsanam, S.: The protein kinase complement of the human genome. Science 298(5600), 1912-1934 (2002)
20. McLachlan, G.J., Peel, D.: Finite Mixture Models. John Wiley & Sons, Chichester (2000)
21. Mirny, L.A., Gelfand, M.S.: Using orthologous and paralogous proteins to identify specificity-determining residues in bacterial transcription factors. J. Mol. Biol. 321(1), 7-20 (2002)
22. Pei, J., Cai, W., Kinch, L.N, Grishin, N.V.: Prediction of functional specificity determinants from protein sequences using log-likelihood ratios. Bioinformatics 22(2), 164-171 (2006)
23. Sjolander, K., Karplus, K., Brown, M., Hughey, R., Krogh, A., Mian, I.S, Haussler, D.: Dirichlet mixtures: A method for improving detection of weak but significant protein sequence homology. Technical report, University of California at Santa Cruz, Santa Cruz, CA, USA (1996)
24. Smith, CM., Shindyalov, I.N., Veretnik, S., Gribskov, M., Taylor, S.S., Ten Eyck, L.F., Bourne, P.E.: The protein kinase resource. Trends Biochem. Sci. 22(11), 444-446 (1997)
25. Wicker, N., Perrin, G.R., Thierry, J.C., Poch, O.: Secator: a program for inferring protein subfamilies from phylogenetic trees. Mol. Biol. Evol. 18(8), 1435-1441 (2001)
26. Yu, G., Park, B.-H., Chandramohan, P., Munavalli, R., Geist, A., Samatova, N.F.: In silico discovery of enzyme-substrate specificity-determining residue clusters. J. Mol. Biol. 352(5), 1105-1117 (2005)
27. Zhang, G., Liu, Y., Ruoho, A.E., Hurley, J.H.: Structure of the adenylyl cyclase catalytic core. Nature 386(6622), 247-253 (1997)