Saliency Map on Cnns for Protein Secondary Structure Prediction

Abstract

Deep learning, a powerful methodology for data-driven modelling, has been shown to be useful in tackling several problems in the biomedical domain. However, deep neural architectures lack interpretability of how predictions from them are made on any test input. While several approaches to "opening the black box" are being developed, their application to biological and medical data is very much as its infancy. Here, we consider the specific problem of protein secondary structure prediction using the techniques of saliency maps to explain decisions of a deep neural network. The analysis leads to two important observations: (a) one-hot-encoded amino-acids are irrelevant in the presence of PSSM values as extra features; and (b) in predicting α-helices at any position, amino-acids to the right are far more important than those to the left. The latter observation may have a biological basis relating to the synthesis of proteins by ribosome movement from left to right, sequentially adding amino-acids.