Version 1
: Received: 15 December 2022 / Approved: 20 December 2022 / Online: 20 December 2022 (03:28:52 CET)
How to cite:
Grešová, K.; Vaculík, O.; Alexiou, P. Using Attribution Sequence Alignment to Interpret Deep Learning Models for Mirna Binding Site Prediction. Preprints2022, 2022120350. https://doi.org/10.20944/preprints202212.0350.v1
Grešová, K.; Vaculík, O.; Alexiou, P. Using Attribution Sequence Alignment to Interpret Deep Learning Models for Mirna Binding Site Prediction. Preprints 2022, 2022120350. https://doi.org/10.20944/preprints202212.0350.v1
Grešová, K.; Vaculík, O.; Alexiou, P. Using Attribution Sequence Alignment to Interpret Deep Learning Models for Mirna Binding Site Prediction. Preprints2022, 2022120350. https://doi.org/10.20944/preprints202212.0350.v1
APA Style
Grešová, K., Vaculík, O., & Alexiou, P. (2022). Using Attribution Sequence Alignment to Interpret Deep Learning Models for Mirna Binding Site Prediction. Preprints. https://doi.org/10.20944/preprints202212.0350.v1
Chicago/Turabian Style
Grešová, K., Ondřej Vaculík and Panagiotis Alexiou. 2022 "Using Attribution Sequence Alignment to Interpret Deep Learning Models for Mirna Binding Site Prediction" Preprints. https://doi.org/10.20944/preprints202212.0350.v1
Abstract
MicroRNAs (miRNAs) are small non-coding RNAs that play a central role in the post-transcriptional regulation of biological processes. miRNAs regulate transcripts by 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 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 human readable visual representation of miRNA:target interactions and can be used as a proxy for further interpretation of biological concepts learned by the neural network. We demonstrate applications of this method in clustering of experimental data into binding classes, as well as using the method to narrow down predicted miRNA binding sites on long transcript sequences. Importantly, the presented method works with any neural network model trained on a two-dimensional representation of interactions and can be easily extended to further domains such as protein-protein interactions.
Keywords
miRNA target prediction; CLASH; deep learning; interpretation; visualization
Subject
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
