Abstract
In this paper, we illustrate an application aimed at predicting protein secondary structure. The proposed system has been devised using PACMAS, a generic architecture designed to support the implementation of applications explicitly tailored for information retrieval tasks. PACMAS agents are autonomous and flexible, and can be personalized, adaptive and cooperative depending on the given application. To investigate the performance of the proposed approach, preliminary experiments have been performed on sequences taken from well-known protein databases.
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Armano, G., Mancosu, G., Orro, A., Saba, M., Vargiu, E. (2005). BIOPACMAS: A Personalized, Adaptive, and Cooperative MultiAgent System for Predicting Protein Secondary Structure. In: Bandini, S., Manzoni, S. (eds) AI*IA 2005: Advances in Artificial Intelligence. AI*IA 2005. Lecture Notes in Computer Science(), vol 3673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558590_59
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DOI: https://doi.org/10.1007/11558590_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29041-4
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