When less is more: Improving classification of protein families with a minimal set of global features

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

Volumn 4645 LNBI, Issue , 2007, Pages 12-24

  Varshavsky, Roy ^a   Fromer, Menachem ^a   Man, Amit ^a   Linial, Michal ^a  

^a HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

Feature selection; Olfactory receptor; Porins protein family; Support vector machines (SVM)

Indexed keywords

CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; FUNCTIONAL GROUPS; MATHEMATICAL MODELS; SUPPORT VECTOR MACHINES;

HYPOTHESIS; OLFACTORY RECEPTOR; PORINS PROTEIN FAMILY;

PROTEINS;

EID: 37249054511     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74126-8_3     Document Type: Conference Paper

References (29)

1. Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped blast and psi-blast: a new generation of protein database search programs. Nucleic Acids Res. 25(17), 3389-3402 (1997)
2. Scheeff, E.D., Bourne, P.E.: Application of protein structure alignments to iterated hidden markov model protocols for structure prediction. BMC Bioinformatics 7, 410 (2006)
3. Portugaly, E., Harel, A., Linial, N., Linial, M.: Everest: automatic identification and classification of protein domains in all protein sequences. BMC Bioinformatics 7, 277 (2006)
4. Gribskov, M., McLachlan, A.D., Eisenberg, D.: Profile analysis: detection of distantly related proteins. PNAS 84(13), 4355-4358 (1987)
5. Yona, G., Levitt, M.: Within the twilight zone: a sensitive profile-profile comparison tool based on information theory. J. Mol. Biol. 315(5), 1257-1275 (2002)
6. Levitt, M., Gerstein, M.: A unified statistical framework for sequence comparison and structure comparison. PNAS 95(11), 5913-5920 (1998)
7. Rost, B.: Topits: threading one-dimensional predictions into three-dimensional structures. Proc. Int. Conf. Intell. Syst. Mol. Biol. 3, 314-321 (1995)
8. Frith, M.C., et al.: The abundance of short proteins in the mammalian proteome. PLoS Genet 2(4), e52 (2006)
9. Friedberg, I., Kaplan, T., Margalit, H.: Glimmers in the midnight zone: characterization of aligned identical residues in sequence-dissimilar proteins sharing a common fold. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8, 162-170 (2000)
10. Wu, C.H., Apweiler, R., Bairoch, A., Natale, D.A., Barker, W.C., Boeckmann, B., Ferro, S., Gasteiger, E., Huang, H., Lopez, R., Magrane, M., Martin, M.J., Mazumder, R., O'Donovan, C., Redaschi, N., Suzek, B.: The universal protein resource (uniprot): an expanding universe of protein information. Nucleic Acids Res. 34(Database issue), D187-D191 (2006)
11. Kunik, V., Solan, Z., Edelman, S., Ruppin, E., Horn, D.: Motif Extraction and Protein Classification. In: IEEE Computational Systems Bioinformatics Conference (CSB'05), pp. 80-85. IEEE Computer Society Press, Los Alamitos (2005)
12. Cai, C.Z., Han, L.Y., Ji, Z.L., Chen, X., Chen, Y.Z.: Svm-prot: Web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res. 31(13), 3692-3697 (2003)
13. Bateman, A., Birney, E., Cerruti, L., Durbin, R., Etwiller, L., Eddy, S.R., GriffithsJones, S., Howe, K.L., Marshall, M., Sonnhammer, E.L.: The pfam protein families database. Nucleic Acids Res. 30(1), 276-280 (2002)
14. Syed, U., Yona, G.: Using a mixture of probabilistic decision trees for direct prediction of protein function. In: Proceedings of RECOMB, pp. 224-234 (2003)
15. Chou, K.C.: Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21(1), 10-19 (2005)
16. Kahsay, R.Y., Gao, G., Liao, L.: An improved hidden markov model for transmembrane protein detection and topology prediction and its applications to complete genomes. Bioinformatics 21(9), 1853-1858 (2005)
17. Chou, K.C., Cai, Y.D.: Predicting protein quaternary structure by pseudo amino acid composition. Proteins 53(2), 282-289 (2003)
18. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157-1182 (2003)
19. Camon, E., Barrell, D., Lee, V., Dimmer, E., Apweiler, R.: The gene ontology annotation (goa) database-an integrated resource of go annotations to the uniprot knowledgebase. In Silico Biol. 4(1), 5-6 (2004)
20. Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147(1), 195-197 (1981)
21. Hulo, N., et al.: The prosite database. Nucleic Acids Res. 34(Database issue), D227-D230 (2006)
22. Gasteiger, E., Gattiker, A., Hoogland, C., Ivanyi, I., Appel, R.D., Bairoch, A.: Expasy: The proteomics server for in-depth protein knowledge and analysis. Nucleic Acids Res. 31(13), 3784-3788 (2003)
23. Jones, D.T.: Protein secondary structure prediction based on position-specific scoring matrices. J. Mol. Biol. 292(2), 195-202 (1999)
24. Eichacker, L.A., Granvogl, B., Mirus, O., Muller, B.C., Miess, C., Schleiff, E.: Hiding behind hydrophobicity. transmembrane segments in mass spectrometry. J. Biol. Chem. 279(49), 50915-50922 (2004)
25. Skoufos, E.: Conserved sequence motifs of olfactory receptor-like proteins may participate in upstream and downstream signal transduction. Receptors Channels 6(5), 401-413 (1999)
26. Henikoff, J.G., et al.: Increased coverage of protein families with the blocks database servers. Nucl. Acids Res. 28(1), 228-230 (2000)
27. Conticello, S.G., Pilpel, Y., Glusman, G., Fainzilber, M.: Position-specific codon conservation in hypervariable gene families. Trends Genet 16(2), 57-59 (2000)
28. Paulsen, I.T., Park, J.H., Choi, P.S., Saier, M.H.: A family of gram-negative bacterial outer membrane factors that function in the export of proteins, carbohydrates, drugs and heavy metals from gram-negative bacteria. FEMS Microbiology Letters 156(1), 1-8 (1997)
29. Chakrabarti, S., Lanczycki, C.J.: Analysis and prediction of functionally important sites in proteins. Protein Sci. 16(1), 4-13 (2007)