title: Feed-forward neural networks for secondary structure prediction.
abstract: A feed-forward neural network has been employed for protein secondary structure prediction. Attempts were made to improve on previous prediction accuracies using a hierarchical mixture of experts (HME). In this method input data are clustered and used to train a series of different networks. Application of an HME to the prediction of protein secondary structure is shown to provide no advantages over a single network. We have also tried various new input representations, chosen to incorporate the effect of residues a long distance away in the one-dimensional amino acid chain. Prediction accuracy using these methods is comparable to that achieved by other neural networks.
authors.affiliation: Physical Chemistry Laboratory, Oxford, England.
authors.forename: T W
authors.initials: TW
authors.lastname: Barlow
mesh_terms: D016571:Neural Networks, Computer; D017433:Protein Structure, Secondary