Interpreting cis -regulatory mechanisms from genomic deep neural networks using surrogate models

Seitz, Evan E, McCandlish, David M, Kinney, Justin B, Koo, Peter K (November 2023) Interpreting cis -regulatory mechanisms from genomic deep neural networks using surrogate models. bioRxiv. (Submitted)

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Abstract

Deep neural networks (DNNs) have greatly advanced the ability to predict genome function from sequence. Interpreting genomic DNNs in terms of biological mechanisms, however, remains difficult. Here we introduce SQUID, a genomic DNN interpretability framework based on surrogate modeling. SQUID approximates genomic DNNs in user-specified regions of sequence space using surrogate models, i.e., simpler models that are mechanistically interpretable. Importantly, SQUID removes the confounding effects that nonlinearities and heteroscedastic noise in functional genomics data can have on model interpretation. Benchmarking analysis on multiple genomic DNNs shows that SQUID, when compared to established interpretability methods, identifies motifs that are more consistent across genomic loci and yields improved single-nucleotide variant-effect predictions. SQUID also supports surrogate models that quantify epistatic interactions within and between cis-regulatory elements. SQUID thus advances the ability to mechanistically interpret genomic DNNs.

Item Type:	Paper
Subjects:	bioinformatics bioinformatics > genomics and proteomics organs, tissues, organelles, cell types and functions > tissues types and functions > neural networks
CSHL Authors:	McCandlish, David Kinney, Justin B. Koo, Peter K Seitz, Evan
Communities:	CSHL labs > Kinney lab CSHL labs > Koo Lab CSHL labs > McCandlish lab
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	16 November 2023
Date Deposited:	20 Dec 2023 20:55
Last Modified:	20 Dec 2023 20:56
PMCID:	PMC10680760
Related URLs:	Publisher
URI:	https://repository.cshl.edu/id/eprint/41358

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