Utilizing Mutations to Evaluate Interpretability of Neural Networks on Genomic Data

TL;DR

This paper evaluates the fidelity of various attribution methods for neural networks on genomic data by using point mutations, finding LRP to be the most accurate in identifying key biological features.

Contribution

It introduces a quantitative mutation-based approach to assess the interpretability of attribution methods in genomic neural network models.

Findings

LRP outperforms other attribution methods in fidelity.

LRP correctly identifies Kozak sequence importance.

LRP detects effects of premature stop codons.

Abstract

Even though deep neural networks (DNNs) achieve state-of-the-art results for a number of problems involving genomic data, getting DNNs to explain their decision-making process has been a major challenge due to their black-box nature. One way to get DNNs to explain their reasoning for prediction is via attribution methods which are assumed to highlight the parts of the input that contribute to the prediction the most. Given the existence of numerous attribution methods and a lack of quantitative results on the fidelity of those methods, selection of an attribution method for sequence-based tasks has been mostly done qualitatively. In this work, we take a step towards identifying the most faithful attribution method by proposing a computational approach that utilizes point mutations. Providing quantitative results on seven popular attribution methods, we find Layerwise Relevance Propagation (LRP) to be the most appropriate one for translation initiation, with LRP identifying two important biological features for translation: the integrity of Kozak sequence as well as the detrimental effects of premature stop codons.