Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites

被引：1398

作者：

Betel, Doron ^{[1
]}

Koppal, Anjali ^{[2
]}

Agius, Phaedra ^{[1
]}

Sander, Chris ^{[1
]}

Leslie, Christina ^{[1
]}

机构：

[1] Mem Sloan Kettering Canc Ctr, Computat Biol Program, New York, NY 10065 USA

[2] Columbia Univ, Dept Comp Sci, New York, NY 10027 USA

来源：

GENOME BIOLOGY | 2010年 / 11卷 / 08期

关键词：

RNA-BINDING PROTEIN; MESSENGER-RNAS; POSTTRANSCRIPTIONAL REGULATION; C; ELEGANS; RECOGNITION; COMPLEMENTARY; ACCESSIBILITY; DETERMINANTS; TRANSCRIPTS; ELEMENTS;

D O I：

10.1186/gb-2010-11-8-r90

中图分类号：

Q81 [生物工程学（生物技术）]; Q93 [微生物学];

学科分类号：

071005 [微生物学]; 090105 [作物生产系统与生态工程];

摘要：

mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.

引用

页数：14

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