Using Attribution Sequence Alignment to Interpret Deep Learning Models for miRNA Binding Site Prediction.

ABSTRACT: MicroRNAs (miRNAs) are small non-coding RNAs that play a central role in the post-transcriptional regulation of biological processes. miRNAs regulate transcripts through direct binding involving the Argonaute protein family. The exact rules of binding are not known, and several in silico miRNA target prediction methods have been developed to date. Deep learning has recently revolutionized miRNA target prediction. However, the higher predictive power comes with a decreased ability to interpret increasingly complex models. Here, we present a novel interpretation technique, called attribution sequence alignment, for miRNA target site prediction models that can interpret such deep learning models on a two-dimensional representation of miRNA and putative target sequence. Our method produces a h

SUBMITTER: Gresova K 

PROVIDER: S-EPMC10045089 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature