DeepMirTar: a deep-learning approach for predicting human miRNA targets

Hongmei Lü, Ming Wen, Peisheng Cong, Zhimin Zhang, Tonghua Li
2018-05-28

Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates. Results: In this study, we reported the design and implementation of DeepMirTar, a deep-learning-based approach for accurately predicting human miRNA targets at the site level. The predicted miRNA-target sites are those having canonical or non-canonical seed, and features, including high-level expert-designed, low-level expert-designed and raw-data-level, were used to represent the miRNA-target site. Comparison with other state-of-the-art machine-learning methods and existing miRNA-target-prediction tools indicated that DeepMirTar improved overall predictive performance. Availability and implementation: DeepMirTar is freely available at https://github.com/Bjoux2/DeepMirTar_SdA. Supplementary information: Supplementary data are available at Bioinformatics online.
Publication Details
Publication Date
2018-05-28
Journal
Publisher
ISSN
Access Type
Author Information
Authors
Hongmei Lü
Ming Wen
Peisheng Cong
Zhimin Zhang
Tonghua Li
Explore further
Open the scid.ai AI chat with a ready-made request: it will find papers on a similar topic and help build a literature review.
Find similar papers in the chat
Make a presentation
100%