PROTEIN SECONDARY STRUCTURE PREDICTION THROUGH A COOPERATIVE MULTIAGENT LEARNING APPROACH
Andrea Addis, Giuliano Armano, Francesco Mascia, Eloisa Vargiu · UNICA IRIS Institutional Research Information System (University of Cagliari) · 2007
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.