Koo, PK, Ploenzke, M (February 2020) Deep learning for inferring transcription factor binding sites. Current Opinion in Systems Biology, 19. pp. 16-23. ISSN 2452-3100
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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.
Item Type: | Paper |
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Subjects: | bioinformatics > computational biology > algorithms > machine learning bioinformatics > genomics and proteomics > genetics & nucleic acid processing > protein structure, function, modification > protein types > transcription factor |
CSHL Authors: | |
Communities: | CSHL labs > Koo Lab CSHL Cancer Center Program > Gene Regulation and Inheritance Program |
SWORD Depositor: | CSHL Elements |
Depositing User: | CSHL Elements |
Date: | 1 February 2020 |
Date Deposited: | 07 May 2021 20:31 |
Last Modified: | 13 Jul 2021 19:04 |
PMCID: | PMC7469942 |
URI: | https://repository.cshl.edu/id/eprint/40067 |
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