Biophysical models of cis-regulation as interpretable neural networks

Tareen, Ammar, Kinney, Justin (November 2019) Biophysical models of cis-regulation as interpretable neural networks. BioRxiv. (Unpublished)

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Abstract

Abstract The adoption of deep learning techniques in genomics has been hindered by the difficulty of mechanistically interpreting the models that these techniques produce. In recent years, a variety of post-hoc attribution methods have been proposed for addressing this neural network interpretability problem in the context of gene regulation. Here we describe a complementary way of approaching this problem. Our strategy is based on the observation that two large classes of biophysical models of cis-regulatory mechanisms can be expressed as deep neural networks in which nodes and weights have explicit physiochemical interpretations. We also demonstrate how such biophysical networks can be rapidly inferred, using modern deep learning frameworks, from the data produced by certain types of massively parallel reporter assays (MPRAs). These results suggest a scalable strategy for using MPRAs to systematically characterize the biophysical basis of gene regulation in a wide range of biological contexts. They also highlight gene regulation as a promising venue for the development of scientifically interpretable approaches to deep learning.

Item Type:	Paper
Subjects:	bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function > gene regulation bioinformatics > genomics and proteomics > genetics & nucleic acid processing > DNA, RNA structure, function, modification > genes, structure and function > gene regulation bioinformatics > computational biology > algorithms > machine learning
CSHL Authors:	Kinney, Justin B. Tareen, Ammar
Communities:	CSHL labs > Kinney lab
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	8 November 2019
Date Deposited:	07 May 2021 14:24
Last Modified:	07 May 2021 14:24
URI:	https://repository.cshl.edu/id/eprint/40047

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