MASSP: A hybrid genetic-neural system for predicting protein secondary structure

Armano G.(1), Mancosu G.(2), Orro A.(1), Saba M.(1), and Vargiu E.(1)
(1) DIEE - Dept. of Electrical and Electronic Engineering, University of Cagliari Piazza d’Armi, I-09123
Cagliari, Italy (2) Shardna Life Sciences, Piazza Deffenu 4, I-09121 Cagliari, Italy

Being the prediction of protein structure a very complex task, most methodologies concentrate on the simplified task of predicting secondary structures. In this paper, we illustrate a technique based on multiple experts, aimed at predicting protein secondary structures. The prediction activity results from the interaction of a population of experts, each integrating genetic and neural technologies. Roughly speaking, an expert of this kind embodies a genetic classifier designed to control the activation of a feedforward artificial neural network for performing a locally-scoped prediction activity. Genetic and neural components (i.e., guard and embedded predictor, respectively) are devoted to perform different tasks and are supplied with different information: Each guard is aimed at (soft-)partitioning the input space, insomuch assuring both the diversity and the specialization of the corresponding embedded predictor, which in turn is devoted to perform the actual prediction.

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