%0 Journal Article %1 Selby_2025 %A Selby, David A. %A Sprang, Maximilian %A Ewald, Jan %A Vollmer, Sebastian J. %D 2025 %J Nature Reviews Genetics %K topic_lifescience yaff %R 10.1038/s41576-025-00826-1 %T Beyond the black box with biologically informed neural networks %U https://doi.org/10.1038/s41576-025-00826-1 %X Machine learning models for multi-omics data often trade off predictive accuracy against biological interpretability. An emerging class of deep learning architectures structurally encode biological knowledge to improve both prediction and explainability. Opportunities and challenges remain for broader adoption.

@article{Selby_2025, abstract = {Machine learning models for multi-omics data often trade off predictive accuracy against biological interpretability. An emerging class of deep learning architectures structurally encode biological knowledge to improve both prediction and explainability. Opportunities and challenges remain for broader adoption.}, added-at = {2025-03-10T16:01:50.000+0100}, author = {Selby, David A. and Sprang, Maximilian and Ewald, Jan and Vollmer, Sebastian J.}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/23c8b824a13365025f1d11ba7593d1c91/scadsfct}, day = 04, doi = {10.1038/s41576-025-00826-1}, interhash = {880dafc589612bf4dcae1d7d7b368790}, intrahash = {3c8b824a13365025f1d11ba7593d1c91}, issn = {1471-0064}, journal = {Nature Reviews Genetics}, keywords = {topic_lifescience yaff}, month = mar, timestamp = {2025-03-10T16:04:42.000+0100}, title = {Beyond the black box with biologically informed neural networks}, url = {https://doi.org/10.1038/s41576-025-00826-1}, year = 2025 }