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Please use this identifier to cite or link to this item: http://hdl.handle.net/10016/9781

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Title: Protein-protein functional association prediction using genetic programming
Author(s): García-Jiménez, Beatriz
Aler, Ricardo
Ledezma, Agapito
Sanchis, Araceli
Publisher: Association for Computing Machinery (ACM)
Issued date: 2008
Citation: GECCO '08 : Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp.347-348.
URI: http://hdl.handle.net/10016/9781
ISBN: 978-1-60558-130-9
DOI: http://dx.doi.org/10.1145/1389095.1389156
Description: Genetic and Evolutionary Computation Conference, GECCO-08. July 12-16, 2008, Atlanta, Georgia, USA.
Abstract: Determining if a group of proteins are functionally associated among themselves is an open problem in molecular biology. Within our long term goal of applying Genetic Programming (GP) to this domain, this paper evaluates the feasibility of GP to predict if a given pair of proteins interacts. GP has been chosen because of its potential flexibility in many aspects, such as the definition of operations. In this paper, the if-unknown operation is defined, which semantically is the most appropriate in this domain for handling missing values. We have also used the Tarpeian bloat control method to decrease the computational time and the solution size. Our results show that GP is feasible for this domain and that the Tarpeian method can obtain large improvements in search efficiency and interpretability of solutions.
Sponsor: Data used in these experiments has been obtained in support of the Structural Computational Biology Group in Spanish National Cancer Research Centre (CNIO). This work has been supported by CICYT (2004-07) TRA2004-07441-C03-02/IA project.
Publisher version: http://dx.doi.org/10.1145/1389095.1389156
Keywords: Algorithms
Measurement
Performance
Experimentation
Rights: © Copyright is held by the author/owner(s).
Appears in Collections:DI - CAOS - Capítulos de Monografías
DI - CAOS - Comunicaciones en Congresos y otros eventos

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