Name
Abstract | ||
|---|---|---|
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 DeepMir Tar, 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 highlevel 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 DeepMir Tar improved overall predictive performance. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1093/bioinformatics/bty424 | BIOINFORMATICS |
Field | DocType | Volume |
Data mining,Computer science,Artificial intelligence,Deep learning,Machine learning | Journal | 34 |
Issue | ISSN | Citations |
22 | 1367-4803 | 3 |
PageRank | References | Authors |
0.41 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming Wen | 1 | 17 | 1.98 |
| Peisheng Cong | 2 | 18 | 2.88 |
| Zhimin Zhang | 3 | 54 | 11.10 |
| Hongmei Lu | 4 | 3 | 0.41 |
| Tonghua Li | 5 | 42 | 2.84 |