Deep learning for inferring transcription factor binding sites

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
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:	Koo, Peter K
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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