Selecting predictive features for recognition of hypersensitive sites of regulatory genomic sequences with an evolutionary algorithm

Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10

Volumn , Issue , 2010, Pages 179-186

  Kamath, Uday ^a   De Jong, Kenneth A ^a   Shehu, Amarda ^a  

^a Krasnow Institute for Advanced Study   (United States)

Author keywords

DNase I Hypersensitive Sites; Evolutionary Algorithms; Support Vector Machines

Indexed keywords

CLASSIFICATION METHODS; COMPUTATIONAL APPROACH; DNA-BINDING; DNASE I HYPERSENSITIVE SITES; EVOLUTIONARY COMPUTATIONS; EXPERIMENTAL EVIDENCE; GENE TRANSCRIPTIONS; GENOMIC SEQUENCE; GENOMICS; HIGH-THROUGHPUT; NOVEL METHODS; PHENOTYPIC DIFFERENCES; PROTEIN EXPRESSIONS; REGULATORY SEQUENCES; SUPPORT VECTOR; SVM CLASSIFICATION;

CALCULATIONS; CATALYSTS; DNA SEQUENCES; ENZYMES; FEATURE EXTRACTION; GENES; SUPPORT VECTOR MACHINES; TRANSCRIPTION;

EVOLUTIONARY ALGORITHMS;

EID: 77955874036     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1830483.1830516     Document Type: Conference Paper

References (43)

1. B. P. Berman, Y. Nibu, B. D. Pfeiffer, P. Tomancak, S. E. Celniker, M. Levine, G. M. Rubin, andM. B. Eisen. Exploiting transcription factor binding site clustering to identify cis-regulatory modules involved in pattern formation in the Drosophila genome. Proc. Natl. Acad. Sci. USA, 98(2): 757-762, 2002.
2. M. Blanchette, A. R. Bataille, X. Chen, C. Poitras, J. Laganiere, C. Lefebvre, G. Deblois, V. Giguere, V. Ferretti, D. Bergeron, B. Coulombe, andF. Robert. Genome-wide computational prediction of transcriptional regulatory modules reveals new insights into human gene expression. Genome Res., 16(5):656-668, 2006.
3. B. E. Boser, I. M. Guyon, andV. N. Vapnik. A training algorithm for optimal margin classifiers. In D. Haussler, editor, 5th Annual ACM Workshop on COLT, pages 144-152. ACM Press, 1992.
4. B. Burgess-Beusse, C. Farrell, M. Gaszner, M. Litt, V. Mutskov, F. Recillas-Targa, M. Simpson, A. West, andG. Felsenfeld. The insulation of genes from external enhancers and silencing chromatin. Proc. Natl. Acad. Sci. USA, 99(S4): 16433-16437, 2002.
5. L. C., E. E., A. Cohen, A. Weston, andW. S. Noble. Mismatch string kernels for SVM protein classification. Neur. Inf. Proc. Sys., 15(4): 1441-1448, 2002.
6. I. Committee. Nomenclature committee of the international union of biochemistry (nc-iub). nomenclature for incompletely specified bases in nucleic acid sequences, recommendations 1984. 229(2): 75-88, 1985.
7. Consortium IHGS. Finishing the euchromatic sequence of the human genome. Nature, 431(7011):931-945, 2004.
8. E. Davidson. Genomic regulatory systems: Development and evolution. Academic Press, New York, NY, 2001.
9. K. A. De Jong. Evolutionary computation: a unified approach. MIT Press, Cambridge, MA, 2001.
10. M. O. Dorschner, M. Hawrylycz, R. Humbert, J. C. Wallace, A. Shafer, J. Kawamoto, J. Mack, R. Hall, J. Goldy, P. J. Sabo, A. Kohli, Q. Li, M. McArthur, andJ. A. Stamatoyannopoulos. High-throughput localization of functional elements by quantitative chromatin profiling. Nat Methods, 1(3):219-225, 2004.
11. R.-E. Fan, P.-H. Chen, andC.-J. Lin. Working set selection using the second order information for training SVM. J. Mach. Learn. Res., 6(1532-4435):1889-1918, 2005.
12. G. Felsenfeld. Chromatin unfolds. Cell, 86(1):13-19, 1996.
13. G. Felsenfeld. Controlling the double helix. Nature, 421 (6921):448-453, 2003.
14. D. S. Gross andW. T. Garrard. Nuclear hypersensitive sites in chromatin. Annu. Rev. Biochem., 57: 159-197, 1988.
15. T. Habib, C. Zhang, J. Y. Yang, M. Q. Yang, andY. Deng. Supervised learning method for the prediction of subcellular localization of proteins using amino acid and amino acid pair composition. BMC Genom., 9(Suppl1):S1-S16, 2008.
16. J. A. Hanley andB. J. McNeil. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143: 29-36, 1982.
17. D. R. Higgs, D. Vernimmen, J. Hughes, andR. Gibbons. Using genomics to study how chromatin influences gene expression. Annu. Rev. Genom. Human Genet., 8: 299-325, 2007.
18. R. C. Holland, T. A. Down, M. Pocock, A. Prlic, D. Huen, K. James, S. Foisy, A. Draeger, A. Yates, M. Heuer, andM. J. Schreiber. BioJava: an open-source framework for bioinformatics. Bioinformatics, 24(18):2096-2097, 2008.
19. M. L. Howard and E. Davidson. cis-Regulatory control circuits in development. Dev. Biol., 271(1):109-118, 2004.
20. R. Islamaj-Dogan, L. Getoor, andW. J. Wilbur. A feature generation algorithm with applications to biological sequence classification. In H. Liu and H. Motoda, editors, Computational Methods of Feature Selection. Springer, Berling, Heiderlberg, 2007.
21. R. Islamaj-Dogan, L. Getoor, W. J. Wilbur, andS. M. Mount. Features generated for computational splice-site prediction correspond to functional elements. BMC Bioinformatics, 8: 410-416, 2007.
22. A. Kernytsky andB. Rost. Using genetic algorithms to select most predictive protein features. Proteins: Struct. Funct. Bioinfi, 75(1):75-88, 2009.
23. M. A. Larkin, G. Blackshields, N. P. Brown, R. Chenna, P. A. McGettigan, H. McWilliam, F. Valentin, I. M. Wallace, A. Wilm, R. Lopez, J. D. Thompson, T. J. Gibson, andD. G. Higgins. Clustalw and clustalx version 2. Bioinformatics, 23(21) :2947-2948, 2007.
24. C. Leslie, E. Eskin, andW. S. Noble. The spectrum kernel: a string kernel for svm protein classification, 2002.
25. C. H. Lowrey, D. M. Bodine, andA. W. Nienhuis. Mechanism of DNase I hypersensitive site formation within the human globin locus control region. Proc. Natl. Acad. Sci. USA, 89(3): 1143-1147, 1992.
26. M.-Z. Lu, C. L. P. Chen, andJ.-B. Huo. Optimization of combined kernel function for svm by particle swarm optimization. In IEEE Intl Conf on Machine Learning and Cybernetics, volume 2, pages 1160-1166, Baoding, China, 2009.
27. M.-Z. Lu, C. L. P. Chen, J.-B. Huo, andX. Wang. Optimization of combined kernel function for svm based on large margin learning theory. In IEEE Intl Conf on Systems, Man and Cybernetics, pages 353-358, Singapore, 2008.
28. G. A. Maston, S. K. Evans, andM. R. Green. Transriptional regulatory elements in the human genome. Annu. Rev. Genom. Human Genet., 7:29-59, 2006.
29. W. S. Noble. Support vector machine applications in computational biology. In B. Schoelkopf, K. Tsuda, and J.-P. Vert, editors, Kernel Methods in Computational Biology. MIT Press, Cambridge, MA, 2004.
30. W. S. Noble, S. Kuehn, R. Thurman, M. Yu, andJ. A. Stamatoyannopoulos. Predicting the in vivo signature of human gene regulatory sequences. Bioinformatics, 21(Suppl 1):i338-i343, 2005.
31. S.-Y. Ohn, H.-N. Nguyen, D. S. Kim, andJ. S. Park. Determining optimal decision model for support vector machine by genetic algorithm. In J. Zhang, J.-H. He, and F. Y., editors, Lecture Notes in Computer Science: Computational and Information Science, volume 3314, pages 895-902. Springer, 2005.
32. A. Prakash, M. Blanchette, S. Sinha, andM. Tompa. Motif discovery in heterogeneous sequence data. In Pac. Symp. Biocomput., pages 348-359, 2004.
33. R. Reed andR. Mark. Neural smithing: supervised learning in feed-forward artificial neurral networks. MIT Press, Cambridge, MA, 1999.
34. P. J. Sabo, R. Humbert, M. Hawrylycz, J. C. Wallace, M. O. Dorschner, M. McArthur, andJ. A. Stamatoyannopoulos. Genome-wide identification of DNase I hypersensitive sites using active chromatin sequence libraries. Proc. Natl. Acad. Sci. USA, 101(13): 4537-4542, 2004.
35. S. Sinha andM. Tompa. Discovery of novel transcription factor binding sites by statistical overrepresentation. Nucl. Acids Res., 30(24):5549-5560, 2002.
36. J. A. Stamatoyannopoulos, A. Goodwin, T. Joyce, and CH. Lowrey. NF-E2 and GATA binding motifs are required for the formation of DNase I hypersensitive site 4 of the human beta-globin locus control region. EMBO J., 14(1):106-116, 1995.
37. D. Talbot, J. N. J. Philipsen, P. Fraser, andF. Grosveld. Detailed analysis of the site 3 region of the human beta-globin dominant control region. EMBO J., 9(7):2169-2177, 1990.
38. M. Tompa, N. Li, T. L. Bailey, G. M. Church, B. De Moor, E. Eskin, A. V. Favorov, M. C. Frith, Y. Fu, W. J. Kent, V. J. Makeev, A. A. Mironov, W. S. Noble, G. Pavesi, G. Pesole, M. Regnier, N. Simonis, S. Sinha, G. Thijs, J. van Helden, M. Vandenbogaert, Z. Weng, C. Workman, C. Ye, andZ. Zhu. Assessing computational tools for the discovery of transcription factor binding sites. Nat. Biotechnol, 23(1):137-144, 2005.
39. V. N. Vapnik. Statistical learning theory. Wiley & Sons, New York, NY, 1998.
40. K. Vertanen. Genetic adventures in parallel: Towards a good island model under PVM, 1998.
41. C Wu. The 5 ends of drosophila heat shock genes in chromatin are hypersensitive to DNase I. Nature, 286(5776) :854-860, 1980.
42. X. H. Zhang, K. A. Heller, I. Hefter, C. S. Leslie, andL. A. Chasin. Sequence information for the splicing of human pre-mrna identified by support vector machine classification. Genome Res., 13(12):2637-2650, 2003.
43. A. Zien, G. Raetsch, S. Mika, B. Schoelkopf, T. Lengauer, andK. R. Mueller. Engineering support vector machine kernels that recognize translation initiation sites. Bioinformatics, 16(9):799-807, 2000.