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Title: Application of Deep Learning Models to microRNA Transcription Start Site Identification
microRNAs (miRNA) are ~22 base pair long RNAs that play important roles in regulating gene expression.Understanding the transcriptional regulation of miRNA is critical to gene regulation. However, it is often difficult to precisely identify miRNA transcription start sites (TSSs) due to miRNA-specific biogenesis. Existing computational methods cannot effectively predict miRNA TSSs. Here, we employed deep learning architectures incorporating Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) techniques to detect miRNA TSSs in regions of accessible chromatin. By testing on benchmark experimental data, we demonstrated that deep learning models outperform support vector machine and can accurately distinguish miRNA TSSs from both flanking regions and intergenic regions.  more » « less
Award ID(s):
1661414
PAR ID:
10104272
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
2019 IEEE 7th International Conference on Bioinformatics and Computational Biology
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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