Publication details
Using Attribution Sequence Alignment to Interpret Deep Learning Models for miRNA Binding Site Prediction
| Authors | |
|---|---|
| Year of publication | 2023 |
| Type | Article in Periodical |
| Magazine / Source | Biology |
| MU Faculty or unit | |
| Citation | |
| Web | https://www.mdpi.com/2079-7737/12/3/369 |
| Doi | http://dx.doi.org/10.3390/biology12030369 |
| Keywords | miRNA target prediction; CLASH; deep learning; interpretation; visualization |
| Description | MicroRNAs are small non-coding RNAs that play a central role in many molecular processes, but the exact rules of their activity are not known. In recent years, deep learning computational methods have revolutionized many fields, including the microRNA field. While making accurate predictions is important in biomedical tasks, it is equally important to understand why models make their predictions. Here, we present a novel interpretation technique for deep learning models that produces human readable visual representation of the knowledge learned by the model. This representation is useful for understanding the model’s decisions and can be used as a proxy for the further interpretation of biological concepts learned by the deep learning model. Importantly, the presented method is not tied to the model or biological domain and can be easily extended. |
| Related projects: |