Interaktivní osnova
👷 Seminar on Machine Learning, Information Retrieval, and Scientific Visualization
[Katarína Grešová]: Using Attribution Sequence Alignment to Interpret Deep Learning Models for MiRNA Binding Site Prediction 16. 3. 2023
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 AGO 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. 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.
Slides
Lecture recordings
Readings
[1] Eva Klimentová, Václav Hejret, Ján Krčmář, Katarína Grešová, Ilektra-Chara Giassa, Panagiotis Alexiou: miRBind: A Deep Learning Method for miRNA Binding Classification: https://doi.
[2] Katarína Grešová, Ondřej Vaculík, Panagiotis Alexiou: Using Attribution Sequence Alignment to Interpret Deep Learning Models for miRNA Binding Site Prediction: https://doi.org/