TargetNet: Functional microRNA Target Prediction with Deep Neural Networks. (arXiv:2107.11381v1 [q-bio.GN])

MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by
binding to target sites of messenger RNAs (mRNAs). While identifying functional
targets of miRNAs is of utmost importance, their prediction remains a great
challenge. Previous computational algorithms have major limitations. They use
conservative candidate target site (CTS) selection criteria mainly focusing on
canonical site types, rely on laborious and time-consuming manual feature
extraction, and do not fully capitalize on the information underlying miRNA-CTS
interactions. In this paper, we introduce TargetNet, a novel deep
learning-based algorithm for functional miRNA target prediction. To address the
limitations of previous approaches, TargetNet has three key components: (1)
relaxed CTS selection criteria accommodating irregularities in the seed region,
(2) a novel miRNA-CTS sequence encoding scheme incorporating extended seed
region alignments, and (3) a deep residual network-based prediction model. The
proposed model was trained with miRNA-CTS pair datasets and evaluated with
miRNA-mRNA pair datasets. TargetNet advances the previous state-of-the-art
algorithms used in functional miRNA target classification. Furthermore, it
demonstrates great potential for distinguishing high-functional miRNA targets.



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