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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2020.Koo.Deep learning transcription factor.pdf

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URL: https://pubmed.ncbi.nlm.nih.gov/32905524/
DOI: 10.1016/j.coisb.2020.04.001

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:
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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