Protein secondary structure prediction using different encoding schemes and neural network architectures

Abstract

Protein secondary structure prediction is very important for drug design, protein engineering and immunological studies. This research uses fully connected multilayer perceptron (MLP) neural network with one, two and three hidden layers to predict protein secondary structure. Orthogonal matrix, BLOSUM62 matrix and hydrophobicity matrix are used for input… (More)
DOI: 10.1117/12.542225

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