miRClassify: An advanced web server for miRNA family classification and annotation

Computers in Biology and Medicine

Volumn 45, Issue 1, 2014, Pages 157-160

  Zou, Quan ^a   Mao, Yaozong ^a   Hu, Lingling ^a   Wu, Yunfeng ^a   Ji, Zhiliang ^a  

^a XIAMEN UNIVERSITY   (China)

Author keywords

Classification; Machine learning; Microrna family

Indexed keywords

FUNCTIONAL ANNOTATION; MICRORNA (MIRNA); MICRORNAS; PHYSIOLOGICAL FUNCTIONS; PRIMARY SEQUENCES; SECONDARY STRUCTURES; WEB SERVERS;

CLASSIFICATION (OF INFORMATION); LEARNING SYSTEMS; WEB SERVICES;

RNA;

MICRORNA;

ARTICLE; CONTROLLED STUDY; INTERNET; MACHINE LEARNING; MOLECULAR GENETICS; NUCLEIC ACID STRUCTURE; PREDICTION; PRIORITY JOURNAL; RANDOM FOREST; RNA SEQUENCE; WEB SERVER;

CLASSIFICATION; MACHINE LEARNING; MICRORNA FAMILY;

COMPUTATIONAL BIOLOGY; DATABASE MANAGEMENT SYSTEMS; INTERNET; MICRORNAS; MOLECULAR SEQUENCE ANNOTATION; SEQUENCE ANALYSIS, RNA; SOFTWARE;

EID: 84892466713     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2013.12.007     Document Type: Article

References (18)

1. Kozomara Ana, et al. miRBase: integrating microRNA annotation and deep-sequencing data. Nucl. Acids Res. 2011, 39:D152-D157.
2. Burge W.S., et al. Rfam 11.0: 10 years of RNA families. Nucl. Acids Res. 2013, 41:D226-D232.
3. Ding Jiandong, et al. miRFam: an effective automatic miRNA classification method based on n-grams and a multiclass SVM. BMC Bioinf. 2011, 12:216.
4. Hu Lingling, et al. Benchmark comparison of ab initio microRNA identification methods and software. Genet. Mol. Res. 2012, 11:4525-4538.
5. Huang Yong, et al. Computational identification and characteristics of novel microRNAs from the silkworm (Bombyx mori L.). Mol. Biol. Rep. 2010, 37:3171-3176.
6. Jiang Peng, et al. MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combine features. Nucl. Acids Res. 2007, 35:W339-W344.
7. Jiang Qinghua, et al. miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucl. Acids Res. 2009, 37:D98-D104.
8. Liu Bing, et al. Identifying miRNAs targets and functions. Briefings Bioinf. 2012, 10.1093/bib/bbs075.
9. Lu Ming, et al. An analysis of human microRNA and disease associations. PLoS One 2008, 3:e3420.
10. Ng Kwang Loong Stanley, Mishra Santosh K. De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures. Bioinformatics 2007, 23:1321-1330.
11. Wei Leyi, et al. Computational analysis of miRNA target identification. Curr. Bioinf. 2012, 7:512-525.
12. Williamson Vernell, et al. Detecting miRNAs in deep-sequencing data: a software performance comparison and evaluation. Briefings Bioinf. 2012, 14:36-45.
13. Xiao Changchun, et al. Lymphoproliferative disease and autoimmunity in mice with increased miR-17-92 expression in lymphocytes. Nat. Immunol. 2008, 9:405-414.
14. Xuan Ping, et al. PlantMiRNAPred: efficient classification of real and pseudo plant pre-miRNAs. Bioinformatics 2011, 27:1368-1376.
15. Xuan Ping, et al. MaturePred: efficient identification of microRNAs within novel plant pre-miRNAs. PLoS One 2011, 6:e27422.
16. Xuan Ping, et al. Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors. PLoS One 2013, 8:e70204.
17. Xue Chenghai, et al. Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinf. 2005, 6:310.
18. Yue Dong, et al. Understanding microRNA regulation: a computational perspective. IEEE Signal Process. Mag. 2012, 29:77-88.