Protein Secondary Structure Prediction through a Cooperative MultiAgent Learning Approach

ADDIS, ANDREA;ARMANO, GIULIANO;MASCIA, FRANCESCO;VARGIU, ELOISA

2007-01-01

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Abstract

This paper illustrates a cooperative multiagent learning approach devised to perform classification or prediction tasks. The resulting system is composed by a population of agents that cooperate and interact in accordance with generic requirements imposed by the adoption of evolutionary computation strategies. As a case study, we consider the typical bioinformatics problem of predicting protein secondary structure.