Please use this identifier to cite or link to this item:
https://www.um.edu.mt/library/oar/handle/123456789/117220
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Grešová, Katarína | - |
dc.contributor.author | Vaculík, Ondřej | - |
dc.contributor.author | Alexiou, Panagiotis | - |
dc.date.accessioned | 2024-01-12T17:43:02Z | - |
dc.date.available | 2024-01-12T17:43:02Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Grešová, K., Vaculík, O., & Alexiou, P. (2023). Using Attribution Sequence Alignment to Interpret Deep Learning Models for miRNA Binding Site Prediction. Biology, 12(3), 369. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/117220 | - |
dc.description.abstract | MicroRNAs (miRNAs) are small non-coding RNAs that play a central role in the posttranscriptional 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 human readable visual representation of miRNA:target interactions and can be used as a proxy for the further interpretation of biological concepts learned by the neural network. We demonstrate applications of this method in the 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. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | MDPI | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | MicroRNA | en_GB |
dc.subject | Deep learning (Machine learning) | en_GB |
dc.subject | Visualization | en_GB |
dc.title | Using attribution sequence alignment to interpret deep learning models for miRNA binding site prediction | en_GB |
dc.type | article | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.3390/biology12030369 | - |
dc.publication.title | Biology | en_GB |
Appears in Collections: | Scholarly Works - FacHScABS |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Using_attribution_sequence_alignment_to_interpret_deep_learning_models_for_miRNA_binding_site_prediction.pdf | 646.98 kB | Adobe PDF | View/Open |
Items in OAR@UM are protected by copyright, with all rights reserved, unless otherwise indicated.