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Building Portfolios for the Protein Structure Prediction Problem

Alejandro Arbelaez 1 Youssef Hamadi 2 Michèle Sebag 1, 3, 4 
3 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : This paper, concerned with the protein structure prediction problem, aims at automatically selecting the Constraint Satisfaction algorithm best suited to the problem instance at hand. The contribution is twofold. Firstly, the selection criterion is the quality (minimal cost) in expectation of the solution found after a fixed amount of time, as opposed to the expected runtime. Secondly, the presented approach, based on supervised Machine Learning algorithms, considers the original description of the protein structure problem, as opposed to the features related to the SAT or CSP encoding of the problem.
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Submitted on : Sunday, September 5, 2010 - 9:42:03 PM
Last modification on : Friday, November 18, 2022 - 9:24:50 AM
Long-term archiving on: : Tuesday, October 23, 2012 - 3:35:27 PM


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  • HAL Id : inria-00515138, version 1


Alejandro Arbelaez, Youssef Hamadi, Michèle Sebag. Building Portfolios for the Protein Structure Prediction Problem. Workshop on Constraint Based Methods for Bioinformatics, Jul 2010, Edinburgh, United Kingdom. ⟨inria-00515138⟩



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