Predicting human microRNA precursors based on an optimized feature subset generated by GA-SVM

Genomics

Volumn 98, Issue 2, 2011, Pages 73-78

  Wang, Yanqiu ^a   Chen, Xiaowen ^a   Jiang, Wei ^a   Li, Li ^b   Li, Wei ^a   Yang, Lei ^a   Liao, Mingzhi ^a   Lian, Baofeng ^a   Lv, Yingli ^a   Wang, Shiyuan ^a   Wang, Shuyuan ^a   Li, Xia ^a  

^a HARBIN MEDICAL UNIVERSITY   (China)

^b TONGJI UNIVERSITY SCHOOL OF MEDICINE   (China)

Author keywords

Classification; Feature selection; Genetic algorithm; Human microRNA precursors; Support vector machine

Indexed keywords

MICRORNA; RNA PRECURSOR;

ACCURACY; ARTICLE; CLASSIFICATION; CLASSIFIER; CONTROLLED STUDY; GENETIC ALGORITHM; PREDICTION; PRIORITY JOURNAL; RNA ANALYSIS; SUPPORT VECTOR MACHINE; VALIDATION PROCESS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BASE SEQUENCE; COMPUTATIONAL BIOLOGY; DATABASES, NUCLEIC ACID; HUMANS; MICRORNAS; MOLECULAR SEQUENCE DATA; NUCLEIC ACID CONFORMATION; RNA PRECURSORS; RNA, SMALL INTERFERING; SEQUENCE ANALYSIS, RNA;

EID: 79960697779     PISSN: 08887543     EISSN: 10898646     Source Type: Journal    
DOI: 10.1016/j.ygeno.2011.04.011     Document Type: Article

References (25)

1. Bartel D.P. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004, 116:281-297.
2. Lee R.C., Feinbaum R.L., Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993, 75:843-854.
3. Griffiths-Jones S., Saini H.K., van Dongen S., Enright A.J. miRBase: tools for microRNA genomics. Nucleic Acids Res. 2008, 36:D154-D158.
4. Miranda K.C., Huynh T., Tay Y., Ang Y.S., Tam W.L., Thomson A.M., Lim B., Rigoutsos I. A pattern-based method for the identification of microRNA binding sites and their corresponding heteroduplexes. Cell 2006, 126:1203-1217.
5. Oulas A., Boutla A., Gkirtzou K., Reczko M., Kalantidis K., Poirazi P. Prediction of novel microRNA genes in cancer-associated genomic regions-a combined computational and experimental approach. Nucleic Acids Res. 2009, 37:3276-3287.
6. Ng K.L., Mishra S.K. De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures. Bioinformatics 2007, 23:1321-1330.
7. Xue C., Li F., He T., Liu G.P., Li Y., Zhang X. Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics 2005, 6:310.
8. Batuwita R., Palade V. microPred: effective classification of pre-miRNAs for human miRNA gene prediction. Bioinformatics 2009, 25:989-995.
9. Chang C.-C., Lin C.-J. LIBSVM: a library for support vector machines 2001.
10. Jiang P., Wu H., Wang W., Ma W., Sun X., Lu Z. MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res. 2007, 35:W339-W344.
11. Nam J.W., Shin K.R., Han J., Lee Y., Kim V.N., Zhang B.T. Human microRNA prediction through a probabilistic co-learning model of sequence and structure. Nucleic Acids Res. 2005, 33:3570-3581.
12. Kadri S., Hinman V., Benos P.V. HHMMiR: efficient de novo prediction of microRNAs using hierarchical hidden Markov models. BMC Bioinformatics 2009, 10(Suppl 1):S35.
13. Hsieh C.H., Chang D.T., Hsueh C.H., Wu C.Y., Oyang Y.J. Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm. BMC Bioinformatics 2010, 11(Suppl 1):S52.
14. Yousef M., Nebozhyn M., Shatkay H., Kanterakis S., Showe L.C., Showe M.K. Combining multi-species genomic data for microRNA identification using a Naive Bayes classifier. Bioinformatics 2006, 22:1325-1334.
15. Sewer A., Paul N., Landgraf P., Aravin A., Pfeffer S., Brownstein M.J., Tuschl T., van Nimwegen E., Zavolan M. Identification of clustered microRNAs using an ab initio prediction method. BMC Bioinformatics 2005, 6:267.
16. Finlay D.D., Nugent C.D., McCullagh P.J., Black N.D. Mining for diagnostic information in body surface potential maps: a comparison of feature selection techniques. Biomed. Eng. Online 2005, 4:51.
17. Peng Y., Wu Z., Jiang J. A novel feature selection approach for biomedical data classification. J. Biomed. Inform. 2010, 43:15-23.
18. Mao K.Z. Feature subset selection for support vector machines through discriminative function pruning analysis. IEEE Trans. Syst. Man Cybern. B Cybern. 2004, 34:60-67.
19. Inza I., Larranaga P., Blanco R., Cerrolaza A.J. Filter versus wrapper gene selection approaches in DNA microarray domains. Artif. Intell. Med. 2004, 31:91-103.
20. Hofacker I.L. Vienna RNA secondary structure server. Nucleic Acids Res. 2003, 31:3429-3431.
21. Zhang B.H., Pan X.P., Cox S.B., Cobb G.P., Anderson T.A. Evidence that miRNAs are different from other RNAs. Cell. Mol. Life Sci. 2006, 63:246-254.
22. Li L., Jiang W., Li X., Moser K.L., Guo Z., Du L., Wang Q., Topol E.J., Rao S. A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset. Genomics 2005, 85:16-23.
23. McCaskill J.S. The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers 1990, 29:1105-1119.
24. Zhao D., Wang Y., Luo D., Shi X., Wang L., Xu D., Yu J., Liang Y. PMirP: a pre-microRNA prediction method based on structure-sequence hybrid features. Artif. Intell. Med. 2010, 49:127-132.
25. Bentwich I., Avniel A., Karov Y., Aharonov R., Gilad S., Barad O., Barzilai A., Einat P., Einav U., Meiri E., Sharon E., Spector Y., Bentwich Z. Identification of hundreds of conserved and nonconserved human microRNAs. Nat. Genet. 2005, 37:766-770.