Identifying TF-MiRNA Regulatory Relationships Using Multiple Features.
MicroRNAs are known to play important roles in the transcriptional and post-transcriptional regulation of gene expression. While intensive research has been conducted to identify miRNAs and their target genes in various genomes, there is only limited knowledge about how microRNAs are regulated. In this study, we construct a pipeline that can infer the regulatory relationships between transcription factors and microRNAs from ChIP-Seq data with high confidence. In particular, after identifying candidate peaks from ChIP-Seq data, we formulate the inference as a PU learning (learning from only positive and unlabeled examples) problem. Multiple features including the statistical significance of the peaks, the location of the peaks, the transcription factor binding site motifs, and the evolutionary conservation are derived from peaks for training and prediction. To further improve the accuracy of our inference, we also apply a mean reciprocal rank (MRR)-based method to the candidate peaks. We apply our pipeline to infer TF-miRNA regulatory relationships in mouse embryonic stem cells. The experimental results show that our approach provides very specific findings of TF-miRNA regulatory relationships.
School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, 220 Handan Road, Shanghai 200433, China; Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, 48824, USA.
Mingyu
M
Shao
Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, 48824, USA.
Yanni
Y
Sun
School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, 220 Handan Road, Shanghai 200433, China.
Shuigeng
S
Zhou
D000818:Animals; D019295:Computational Biology; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D016678:Genome; D051379:Mice; D035683:MicroRNAs / Q000235:genetics*; D014157:Transcription Factors / Q000235:genetics*
D035683:MicroRNAs; D014157:Transcription Factors