HuMiTar: A sequence-based method for prediction of human microRNA targets

Background: MicroRNAs (miRs) are small noncoding RNAs that bind to complementary/partially complementary sites in the 3' untranslated regions of target genes to regulate protein production of the target transcript and to induce mRNA degradation or mRNA cleavage. The ability to perform accurate, high-throughput identification of physiologically active miR targets would enable functional characterization of individual miRs. Current target prediction methods include traditional approaches that are based on specific base-pairing rules in the miR's seed region and implementation of cross-species conservation of the target site, and machine learning (ML) methods that explore patterns that contrast true and false miR-mRNA duplexes. However, in the case of the traditional methods research shows that some seed region matches that are conserved are false positives and that some of the experimentally validated target sites are not conserved. Results: We present HuMiTar, a computational...

• Type: Journal
• Year: 2008
• Where: ALMOB
• Authors: Jishou Ruan, Hanzhe Chen, Lukasz A. Kurgan, Ke Chen 0003, Chunsheng Kang, Peiyu Pu
• Keywords: ALMOB 2008, MiR Targets, Seed Region, Target Sites