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A Multi-agent System for Protein Secondary Structure Prediction

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Transactions on Computational Systems Biology III

Part of the book series: Lecture Notes in Computer Science ((TCSB,volume 3737))

Abstract

In this paper, we illustrate a system aimed at predicting protein secondary structures. Our proposal falls in the category of multiple experts, a machine learning technique that –under the assumption of absent or negative correlation in experts’ errors– may outperform monolithic classifier systems. 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. 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. Guards deal with inputs that encode information strictly related with relevant domain knowledge, whereas embedded predictors process other relevant inputs, each consisting of a limited window of residues. To investigate the performance of the proposed approach, a system has been implemented and tested on the RS126 set of proteins. Experimental results point to the potential of the approach.

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Authors and Affiliations

  1. University of Cagliari, Piazza d’Armi, I-09123, Cagliari, Italy

    Giuliano Armano, Alessandro Orro & Eloisa Vargiu

  2. Shardna Life Sciences, Piazza Deffenu 4, I-09121, Cagliari, Italy

    Gianmaria Mancosu

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  1. Giuliano Armano
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  2. Gianmaria Mancosu
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  3. Alessandro Orro
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  4. Eloisa Vargiu
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Editor information

Editors and Affiliations

  1. Centre for Computational and Systems Biology, The Microsoft Research - University of Trento, Piazza Manci, 17, 38050, Povo (TN), Italy

    Corrado Priami

  2. Dipartimento di Matematica e Informatica, Università di Camerino, I-62032, Camerino, Italy

    Emanuela Merelli

  3. DEIS, Università di Bologna, Via Venezia 52, 47023, Cesena, Italy

    Pablo Gonzalez

  4. Alma Mater Studiorum Università di Bologna a Cesena,, via Venezia 52, 47023, Cesena, Italy

    Andrea Omicini

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Armano, G., Mancosu, G., Orro, A., Vargiu, E. (2005). A Multi-agent System for Protein Secondary Structure Prediction. In: Priami, C., Merelli, E., Gonzalez, P., Omicini, A. (eds) Transactions on Computational Systems Biology III. Lecture Notes in Computer Science(), vol 3737. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11599128_2

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  • DOI: https://doi.org/10.1007/11599128_2

  • Publisher Name: Springer, Berlin, Heidelberg

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  • Online ISBN: 978-3-540-31446-2

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