Predicting protein cellular localization sites with a hardware analog neural network

Abstract

This paper presents experimental results obtained during the training of an analog hardware neural network for the prediction of cellular localization sites of proteins in yeast and E.coli. The synaptic weights of the network are optimized by an evolutionary chip-in-the-loop algorithm. The results provide a first demonstration of the applicability of the presented neural network architecture to real-world problems that require the ability to generalize and to handle quasi-continuous multi-bit inputs.