17 Nam J W, Shin K R, Han J, et al. Human microRNA prediction through a probabilistic co-learning model of sequence and structure. NucleicAcids Res, 2005, 33: 3570-3581？？
[2]
18 Sewer A, Paul N, Landgraf P, et al. Identification of clustered microRNAs using an ab initio prediction method. BMC Bioinformatics, 2005,6: 267？？
[3]
19 Jiang P, Wu H, Wang W, et al. MiPred: classification of real and pseudo microRNA precursors using random forest prediction model withcombined features. Nucl Acids Res, 2007, 35: W339-W344
[4]
20 Morin R D, O’Connor M D, Griffith M, et al. Application of massively parallel sequencing to microRNA profiling and discovery in humanembryonic stem cells. Genome Res, 2008, 18: 610-621？？
[5]
21 Bar M, Wyman S K, Fritz B R, et al. MicroRNA discovery and profiling in human embryonic stem cells by deep sequencing of small RNAlibraries. Stem Cells, 2008, 26: 2496-2505？？
[6]
22 Friedl？nder M R, Chen W, Adamidi C, et al. Discovering microRNAs from deep sequencing data using miRDeep. Nat Biotechnol, 2008, 26:407-415？？
24 Bentwich I, Avniel A, Karov Y, et al. Identification of hundreds of conserved and nonconserved human microRNAs. Nat Genet, 2005, 37:766-770？？
[9]
1 Bartel D P. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell, 2004, 116: 281-297？？
[10]
2 Seila A C, Sharp P A. Small RNAs tell big stories in Whistler. Nat Cell Biol, 2008, 10: 630-633？？
[11]
3 Voorhoeve P M, le Sage C, Schrier M, et al. A genetic screen implicates miRNA-372 and miRNA-373 as oncogenes in testicular germ celltumors. Cell, 2006, 124: 1169-1181？？
[12]
4 Tavazoie S F, Alarcon C, Oskarsson T, et al. Endogenous human microRNAs that suppress breast cancer metastasis. Nature, 2008, 451:147-152？？
[13]
5 Yu F, Yao H, Zhu P, et al. let-7 regulates self renewal and tumorigenicity of breast cancer cells. Cell, 2007, 131: 1109-1123？？
[14]
6 Huang Q, Gumireddy K, Schrier M, et al. The microRNAs miR-373 and miR-520c promote tumour invasion and metastasis. Nat Cell Biol,2008, 10: 202-210？？
[15]
7 Kozomara A, Griffiths-Jones S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res, 2011, 39:D152-D157？？
[16]
8 Griffiths-Jones S, Saini H K, van Dongen S, et al. miRBase: tools for microRNA genomics. Nucl Acids Res, 2008, 36: D154-D158？？
[17]
9 Yousef M, Nebozhyn M, Shatkay H, et al. Combining multi-species genomic data for microRNA identification using a Naive Bayesclassifier. Bioinformatics, 2006, 22: 1325-1334？？
[18]
10 Mignone F, Grillo G, Licciulli F, et al. UTRdb and UTRsite: a collection of sequences and regulatory motifs of the untranslated regions ofeukaryotic mRNAs. Nucleic Acids Res, 2005, 33: D141-D146？？
[19]
11 Hofacker I L, Fontana W, Stadler P F, et al. Fast folding and comparison of RNA secondary structures. Monatsh Chem, 1994, 125: 167-188？？
[20]
12 Benson D A, Karsch-Mizrachi I, Lipman D J, et al. GenBank. Nucleic Acids Res, 2011, 39: D32-D37
[21]
13 Bonnet E, Wuyts J, Rouze P, et al. Evidence that microRNA precursors, unlike other non-coding RNAs, have lower folding free energiesthan random sequences. Bioinformatics, 2004, 20: 2911-2917？？
[22]
16 Ng K L S, Mishra S K. De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsicfolding measures. Bioinformatics, 2007, 23: 1321-1330？？
[23]
14 Chang C C, Lin C J. LIBSVM: a Library for Support Vector Machine. 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
[24]
15 Xue C, Li F, He T, et al. Classification of real and pseudo microRNA precursors using local structure-sequence features and support vectormachine. BMC Bioinformatics, 2005, 6: 310？？
