Explaining Deep Neural Networks for the Prediction of Translation Rates

Abstract

AbstractA recent convolutional neural network model accurately quantifies the relationship between massively parallel synthetic 5’ untranslated regions (5’UTRs) and translation levels, but the underlying sequence determinants remain elusive. Applying model interpretation, we extract representations of regulatory logic, revealing a complex interplay of regulatory sequence elements. Guided by insights from model interpretation, we adapt the model by human reporter data to obtain superior performance, which will promote applications in synthetic biology and precision medicine.