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Updated: Feb 9, 2026

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library
Published on: April 6, 2012
DeepMirTar: a deep-learning approach for predicting human miRNA targets
Ming Wen1, Peisheng Cong2, Zhimin Zhang1
1College of Chemistry and Chemical Engineering, Central South University, Changsha, People's Republic of China.
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.
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