Cepred: predicting the co-expression patterns of the human intronic microRNAs with their host genes.
Identifying the tissues in which a microRNA is expressed could enhance the understanding of the functions, the biological processes, and the diseases associated with that microRNA. However, the mechanisms of microRNA biogenesis and expression remain largely unclear and the identification of the tissues in which a microRNA is expressed is limited. Here, we present a machine learning based approach to predict whether an intronic microRNA show high co-expression with its host gene, by doing so, we could infer the tissues in which a microRNA is high expressed through the expression profile of its host gene. Our approach is able to achieve an accuracy of 79% in the leave-one-out cross validation and 95% on an independent testing dataset. We further estimated our method through comparing the predicted tissue specific microRNAs and the tissue specific microRNAs identified by biological experiments. This study presented a valuable tool to predict the co-expression patterns between human intronic microRNAs and their host genes, which would also help to understand the microRNA expression and regulation mechanisms. Finally, this framework can be easily extended to other species.
Wang
Dong
D
Department of Medical Informatics, Peking University Health Science Center, Beijing, China.
Lu
Ming
M
Miao
Jing
J
Li
Tingting
T
Wang
Edwin
E
Cui
Qinghua
Q
D000465:Algorithms; D030561:Databases, Nucleic Acid; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D015894:Genome, Human / Q000235:genetics; D006801:Humans; D007438:Introns / Q000235:genetics*; D035683:MicroRNAs / Q000235:genetics*; D009928:Organ Specificity; D015203:Reproducibility of Results
D035683:MicroRNAs