  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.
