Neural network based prediction of protein structure and Function: Comparison with other machine learning methods

We have utilized neural networks in different applications of bioinformatics such as discrimination of beta-barrel membrane proteins, mesophilic and thermophilic proteins, different folding types of globular proteins, different classes of transporter proteins and predicting the secondary structures of beta-barrel membrane proteins. In these methods, we have used the information about amino acid composition, neighboring residue information, inter-residue contacts and amino acid properties as features. We observed that the performance with neural networks is comparable to or better than other widely used machine learning techniques.

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