Identifying translation initiation sites in prokaryotes using support vector machine

Journal of Theoretical Biology

Volumn 262, Issue 4, 2010, Pages 644-649

  Gao, Tingting ^a   Yang, Zhixia ^b,c   Wang, Yong ^c   Jing, Ling ^a  

^a CHINA AGRICULTURAL UNIVERSITY   (China)

^b XINJIANG UNIVERSITY   (China)

^c CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

Position specific weight matrix; Support vector machine; Translation initiation site prediction

Indexed keywords

ALGORITHM; BIOINFORMATICS; GENOME; MODELING; PROKARYOTE;

ACCURACY; ARTICLE; BACILLUS SUBTILIS; BACTERIAL GENOME; BURKHOLDERIA PSEUDOMALLEI; COMPUTER PREDICTION; CONTROLLED STUDY; ESCHERICHIA COLI; GENE IDENTIFICATION; GENE STRUCTURE; GENETIC DATABASE; INTERMETHOD COMPARISON; MACHINE LEARNING; PRIORITY JOURNAL; PROKARYOTE; PSEUDOMONAS AERUGINOSA; SEQUENCE ANALYSIS; SUPPORT VECTOR MACHINE; TRANSLATION INITIATION SITE; VALIDATION PROCESS;

AREA UNDER CURVE; BACILLUS SUBTILIS; BURKHOLDERIA PSEUDOMALLEI; COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; ESCHERICHIA COLI; FALSE POSITIVE REACTIONS; GENETIC VECTORS; GENOME, BACTERIAL; PATTERN RECOGNITION, AUTOMATED; PROTEIN BIOSYNTHESIS; PROTEIN STRUCTURE, TERTIARY; PSEUDOMONAS AERUGINOSA; ROC CURVE;

BURKHOLDERIA PSEUDOMALLEI; PROKARYOTA; PSEUDOMONAS AERUGINOSA;

EID: 77649339338     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2009.10.023     Document Type: Article

References (21)

1. Besemer J., Lomsadze A., Borodovsky M. GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Research 2001, 29:2607.
2. Bradley A.P. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 1997, 30:1145-1159.
3. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 1998, 2:121-167.
4. Deng N.Y., Tian Y.J. A New Method in Data Mining: Support Vector Machine 2004, Science Press, Beijing.
5. Frishman D., Mironov A., Mewes H.W., Gelfand M. Combining diverse evidence for gene recognition in completely sequenced bacterial genomes. Nucleic Acids Research 1998, 26:2941-2947.
6. Gao, T., Tian, Y., Shao, X., Deng, N., 2008. Accurate prediction of translation initiation sites by Universum SVM. In: Proceedings of the Second International Symposium on Optimization and Systems Biology, vol. 9, Lijiang, pp. 275-282.
7. Hanley J.A., McNeil B.J. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982, 143:29.
8. Hu G.Q., Zheng X., Ju L.N., Zhu H., She Z.S. Computational evaluation of TIS annotation for prokaryotic genomes. BMC Bioinformatics 2008, 9:160.
9. Makita Y., de Hoon M.J.L., Danchin A. Hon-yaku: a biology-driven Bayesian methodology for identifying translation initiation sites in prokaryotes. BMC Bioinformatics 2007, 8:47.
10. Ou H.Y., Guo F.B., Zhang C.T. GS-Finder: a program to find bacterial gene start sites with a self-training method. International Journal of Biochemistry and Cell Biology 2004, 36:535-544.
11. Qing G., Xia B., Inouye M. Enhancement of translation initiation by A/T-rich sequences downstream of the initiation codon in Escherichia coli. Journal of Molecular Microbiology and Biotechnology 2003, 6:133-144.
12. Rocha E.P.C., Viari A., Danchin A. Oligonucleotide bias in Bacillus subtilis: general trends and taxonomic comparisons. Nucleic Acids Research 1998, 26:2971.
13. Spackman K.A. Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning 1989, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 160-163.
14. Suzek B.E., Ermolaeva M.D., Schreiber M., Salzberg S.L. A probabilistic method for identifying start codons in bacterial genomes. Bioinformatics 2001, 17:1123-1130.
15. Tech M., Meinicke P. An unsupervised classification scheme for improving predictions of prokaryotic TIS. BMC Bioinformatics 2006, 7:121.
16. Vapnik V. Estimation of Dependences Based on Empirical Data 2006, Springer.
17. Vapnik V.N. The Nature of Statistical Learning Theory 2000, Springer.
18. Zhao X.M., Wang Y., Chen L., Aihara K. Gene function prediction using labeled and unlabeled data. BMC Bioinformatics 2008, 9:57.
19. Zhao X.M., Li X., Chen L., Aihara K. Protein classification with imbalanced data. Proteins: Structure, Function, and Bioinformatics 2008, 70.
20. Zhu H., Hu G.Q., Yang Y.F., Wang J., She Z.S. MED: a new non-supervised gene prediction algorithm for bacterial and archaeal genomes. BMC Bioinformatics 2007, 8:97.
21. Zhu H.Q., Hu G.Q., Ouyang Z.Q., Wang J., She Z.S. Accuracy improvement for identifying translation initiation sites in microbial genomes. Bioinformatics 2004, 20:3308-3317.