Application of a neural network with a modular architecture to prediction of protein secondary structures-overlearning effects on predictions

The prediction of globular protein secondary structures is studied by a neural network. The application of a neural network with a modular architecture to prediction of protein secondary structures (α-helix, β-sheet and coil) is presented. Each module is a three layer neural network. The results from the neural network with a modular architecture and with a simple three layer structure are compared. Overlearning effect is investigated in ordinary and modular neural networks. The prediction accuracy by a neural network with a modular architecture is higher than of the ordinary neural network. Furthermore, the 3, 4 and 8 state classification scheme of secondary structures are considered in the ordinary three layer neural network. The percentage of correct prediction depends on these state classification method.