Peptide programs: Applying fragment programs to protein classification

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2008, Pages 37-44

  Falcao, Andre O ^a   Faria, Daniel ^a   Ferreira, António ^a  

^a UNIVERSITY OF LISBON   (Portugal)

Author keywords

Bioinformatics; Classification; Machine learning; Proteins

Indexed keywords

ALIGNMENT METHODS; BASIC CONCEPTS; BINARY CLASSIFICATION; BINARY CLASSIFIERS; CENTRAL PROBLEMS; CLASSIFICATION; COMPUTATIONAL RESOURCES; COMPUTER PROGRAM; CORRELATION COEFFICIENT; ENZYME COMMISSIONS; FEATURE SPACE; INSTRUCTION SET; KERNEL FUNCTION; MACHINE LEARNING; PROTEIN CLASSIFICATION; SEQUENCE DATA; VIRTUAL MACHINES;

ALIGNMENT; AMINATION; AMINES; AMINO ACIDS; BIOINFORMATICS; COMPUTER AIDED DIAGNOSIS; EDUCATION; ENZYMES; KNOWLEDGE MANAGEMENT; ORGANIC ACIDS; PEPTIDES; ROBOT LEARNING;

DATA MINING;

EID: 68949093215     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1458449.1458459     Document Type: Conference Paper

References (36)

1. Altschul, S. F., Madden, T. L., Schaffer, A. A., Zhang, J., Zhang, Z., Miller, W. and Lipman, D. J. 1997. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs, Nucleic Acids Res 25(17), (1997),pp. 3389-3402.
2. Devos, D. and Valencia, A. 2000. Practical limits of function prediction, Proteins 41, (2000),pp. 98-107.
3. Tian, W. and Skolnick, J. 2003. How well is enzyme function conserved as a function of pairwise sequence identity?, J Mol Biol 333, (2003),pp. 863-882.
4. Devos, D. and Valencia, A. 2001. Intrinsic errors in genome annotation, Trends Genet 17(8), (2001),pp. 429-431.
5. Baker, E. N., Arcus, V. L. and Lott, J. S. 2003. Protein structure prediction and analysis as a tool for functional genomics, Appl Bioinformatics 2(3), (2003),pp. 3-10.
6. von Grotthuss, M., Plewczynski, D., Ginalski, K., Rychlewski, L. and Shakhnovich, E. I. 2006. PDB-UF: database of predicted enzymatic functions for unannotated protein structures from structural genomics, BMC Bioinformatics 7, (2006),pp. 53.
7. Whisstock, J. C. and Lesk, A. M. 2003. Prediction of protein function from protein sequence and structure, Q Rev Biophys 36(3), (2003),pp. 307-340.
8. Friedberg, I. 2006. Automated protein function prediction the genomic challenge, Brief Bioinform 7(3), (2006),pp. 225-242.
9. Eskin, E., Noble, W. S. and Singer, Y. 2003. Protein family classification using sparse Markov transducers, J Comput Biol 10, (2003),pp. 187-213.
10. Melvin, I., Ie, E., Wetson, J., Noble, W. S. and Leslie C. 2007. Multi-class protein classification using adaptive codes, J Mach Learn Res 8, (2007),pp. 1557-1581.
11. Rangwala, H. and Karypis, G. 2005. Profile-based direct kernels for remote homology detection and fold recognition, Bioinformatics 21(23), (2005),pp. 4239-4247.
12. Han, L. Y., Cai, C. Z., Ji, Z. L., Cao, Z. W., Cui, J. and Chen, Y. Z. 2004. Predicting functional family of novel enzymes irrespective of sequence similarity: a statistical learning approach, Nucleic Acids Res 32(21), (2004),pp. 6437-6444.
13. Bhardwaj, N., Langlois, R. E., Zhao, G. J. and Lu, H. 2005. Kernel-based machine learning protocol for predicting DNAbinding proteins, Nucleic Acids Res 33, (2005),pp. 6486-6493.
14. Cai, C. Z., Han, L. Y., Ji, Z. L. and Chen, Y. Z. 2004. Enzyme family classification by support vector machines, Proteins 55, (2004),pp. 66-76.
15. Cai, Y. D. and Doig, A. J. 2004. Prediction of Saccharomyces cerevisiae protein functional class from functional domain composition, Bioinformatics 20, (2004),pp. 1292-1300.
16. Dobson, P. D. and Doig, A. J. 2005. Predicting enzyme class from protein structure without alignments, J Mol Biol 345, (2005),pp. 187-199.
17. Dong, Q. W., Wang, X. L. and Lin, L. 2005. Application of latent semantic analysis to protein remote homology detection, Bioinformatics 22, (2005),pp. 285-290.
18. Kuang, R., Ie, E., Wang, K., Wang, K., Siddiqi, M., Freund, Y. and Leslie, C. 2005. Profile-based string kernels for remote homology detection and motif extraction, J Bioinform Comput Biol 3(3), (2005),pp. 527-550.
19. Lewis, D. P., Jebara, T. and Noble, W. S. 2006. Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure, Bioinformatics 22, (2006),pp. 2753-2760.
20. Nanni, L. and Lumini, A. 2007. Ensemblator: An ensemble of classifiers for reliable classification of biological data, Pattern Recogn Lett 28, (2007),pp. 622-630.
21. Webb-Robertson, B. J., Oehmen, C. and Matzke, M. 2005. SVM-BALSA: Remote homology detection based on Bayesian sequence alignment, Comput Biol Chem 29, (2005),pp. 440-443.
22. Zhang, Z. D., Kochhar, S. and Grigorov, M. G. 2005. Descriptor-based protein remote homology identification, Protein Sci 14, (2005),pp. 431-444.
23. Langlois, R. E., Carson, M. B., Bhardwaj, N. and Lu, H. 2007. Learning to translate sequence and structure to function: Identifying DNA binding and membrane binding proteins, Ann Biomed Eng 35, (2007),pp. 1043-1052.
24. Yang, M. Q., Yang, J. Y. and Ersoy, O. K. 2007. Classification of proteins multiple-labelled and singlelabelled with protein functional classes, Int J Gen Syst 36, (2007),pp. 91-109.
25. Pasquier, C., Promponas, V. and Hamodrakas, S. J. 2001. PRED-CLASS: Cascading Neural networks for generalized protein classification and genome wide applications, Proteins 44, (2001),pp. 361-369.
26. Wu, C., Berry, M., Shivakumar, S. and McLarty, J. 1995. Neural Networks for Full-Scale Protein Sequence Classification: Sequence Encoding with Singular Value Decomposition J Mach Learn, 21 177-193, (1995).
27. Yang, Z. R. and Hamer, R. 2007. Bio-basis function neural networks in protein data mining, Curr Pharm Design 13, (2007),pp. 1403-1413.
28. Falcao, A. O. 2005. Residue fragment programs for enzyme classification, In Proceedings of the workshop Bioinformatics: Knowledge Discovery in Biology 24-28, (2005).
29. Rost, R. J. 2006 OpenGL Shading Language. 2nd Edition, Addison and Wesley Professional, 2006.
30. Koza, J. 1992 Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT press, 1992.
31. Lavrac, N. and S, D. 1994 Inductive Logic Programming: Techniques and Applications, Ellis Horwood, 1994.
32. Baldi, P., Brunak, S., Chauvin, Y., Anderson, C. A. F. and Nielsen, H. 2000. Assessing the accuracy of prediction algorithms for classification: an overview, Bioinformatics 16, (2000),pp. 412-419.
33. Matthews, B. W. 1975. Comparison of the predicted and observed secondary structure of T4 phage lysozyme, Biochim Biophys Acta 405, (1975),pp. 442-451.
34. Bairoch, A. and Boeckman, B. 1997. The SWISS-PROT protein sequence data bank, recent developments Nucleic Acids Res, (1997),pp. 21.
35. Rappas, M., Niwa, H. and Zhang, X. 2004. Mechanisms of ATPases - a multi-disciplinary approach, Curr Protein Pept Sci 5(2), (2004),pp. 89-105.
36. Freund, Y. and Schapire, R. E. 1997. A decision-theoretic generalization of on-line learning and an application to boosting, J Comput Syst Sci 55(1), (1997),pp. 119-139.