期刊
CURRENT OPINION IN SYSTEMS BIOLOGY
卷 19, 期 -, 页码 16-23出版社
ELSEVIER
DOI: 10.1016/j.coisb.2020.04.001
关键词
Deep learning; Transcription factor binding; Motifs; Neural networks; Interpretability
资金
- NCI Cancer Center Support Grant [CA045508]
- Simons Center for Quantitative Biology at Cold Spring Harbor Laboratory
- NIH NCI [RFA-CA-19-002]
Deep learning is a powerful tool for predicting transcription factor binding sites from DNA sequence. Despite their high predictive accuracy, there are no guarantees that a high-performing deep learning model will learn causal sequencefunction relationships. Thus, a move beyond performance comparisons on benchmark data sets is needed. Interpreting model predictions is a powerful approach to identify which features drive performance gains and ideally provide insight into the underlying biological mechanisms. Here, we highlight timely advances in deep learning for genomics, with a focus on inferring transcription factor binding sites. We describe recent applications, model architectures, and advances in 'local' and 'global' model interpretability methods and then conclude with a discussion on future research directions.
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