Interpreting Deep Neural Networks Beyond Attribution Methods: Quantifying Global Importance of Genomic Features

Koo, Peter K, Ploenzke, Matt (February 2020) Interpreting Deep Neural Networks Beyond Attribution Methods: Quantifying Global Importance of Genomic Features. bioRxiv. (Submitted)

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Abstract

Despite deep neural networks (DNNs) having found great success at improving performance on various prediction tasks in computational genomics, it remains difficult to understand why they make any given prediction. In genomics, the main approaches to interpret a high-performing DNN are to visualize learned representations via weight visualizations and attribution methods. While these methods can be informative, each has strong limitations. For instance, attribution methods only uncover the independent contribution of single nucleotide variants in a given sequence. Here we discuss and argue for global importance analysis which can quantify population-level importance of putative features and their interactions learned by a DNN. We highlight recent work that has benefited from this interpretability approach and then discuss connections between global importance analysis and causality.

Item Type:	Paper
Subjects:	bioinformatics bioinformatics > genomics and proteomics organs, tissues, organelles, cell types and functions > tissues types and functions > neural networks
CSHL Authors:	Koo, Peter K
Communities:	CSHL labs > Koo Lab
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	20 February 2020
Date Deposited:	20 Dec 2023 20:00
Last Modified:	20 Dec 2023 20:00
Related URLs:	Publisher
URI:	https://repository.cshl.edu/id/eprint/41349

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