Global Importance Analysis: An Interpretability Method to Quantify Importance of Genomic Features in Deep Neural Networks

ABSTRACT Deep neural networks have demonstrated improved performance at predicting the sequence specificities of DNA- and RNA-binding proteins compared to previous methods that rely on k -mers and position weight matrices. To gain insights into why a DNN makes a given prediction, model interpretability methods, such as attribution methods, can be employed to identify motif-like representations along a given sequence. Because explanations are given on an individual sequence basis and can vary substantially across sequences, deducing generalizable trends across the dataset and quantifying their effect size remains a challenge. Here we introduce global importance analysis (GIA), a model interpretability method that quantifies the population-level effect size that putative patterns have on model predictions. GIA provides an avenue to quantitatively test hypotheses of putative patterns and their interactions with other patterns, as well as map out specific functions the network has learned. As a case study, we demonstrate the utility of GIA on the computational task of predicting RNA-protein interactions from sequence. We first introduce a convolutional network, we call ResidualBind, and benchmark its performance against previous methods on RNAcompete data. Using GIA, we then demonstrate that in addition to sequence motifs, ResidualBind learns a model that considers the number of motifs, their spacing, and sequence context, such as RNA secondary structure and GC-bias

Small Firms’ Formalisation: The Stick Treatment

<p>Firm informality is pervasive throughout the developing world, Bangladesh being no exception. The informal status of many firms substantially reduces the tax basis and therefore impacts the provision of public goods. The literature on encouraging formalisation has predominantly focused on reducing the direct costs of formalisation and has found negligible impacts of such policies. In this paper, we focus on a stick intervention, which to the best of our knowledge is the first one in a developing country setting that deals with the most direct and dominant form of informality, that is registration with the tax authority with a direct link to the country’s potential revenue base and thus public goods provision. We implement an experiment in which randomly selected firms are visited by tax representatives who deliver an official letter from the Bangladesh National Tax Authority stating that the firm is not registered and the consequential punishment if the firm fails to register. We find that the intervention increases the rate of registration among treated firms, while firms located in the same market but not treated do not seem to respond significantly. We also find that only larger revenue firms at baseline respond to the threat and register. Our findings have at least two important policy implications: i) the enforcement angle, which could be an important tool to encourage formalisation; and ii) targeting of government resources for formalisation to high-end informal firms. The effects are generally small in levels and this leaves open the question of why many firms still do not register.</p