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• W3035587935 abstract "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 sequence-function 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." @default.
• W3035587935 created "2020-06-19" @default.
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• W3035587935 date "2020-02-01" @default.
• W3035587935 modified "2023-10-04" @default.
• W3035587935 title "Deep learning for inferring transcription factor binding sites" @default.
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• W3035587935 doi "https://doi.org/10.1016/j.coisb.2020.04.001" @default.
• W3035587935 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7469942" @default.
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