Paper published in a book (Scientific congresses and symposiums)
Supervised learning to tune simulated annealing for in silico protein structure prediction
Marcos Alvarez, Alejandro; Maes, Francis; Wehenkel, Louis
2012 • In Verleysen, Michel (Ed.) ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
[en] Simulated annealing is a widely used stochastic optimization algorithm whose efficiency essentially depends on the proposal distribu- tion used to generate the next search state at each step. We propose to adapt this distribution to a family of parametric optimization problems by using supervised machine learning on a sample of search states derived from a set of typical runs of the algorithm over this family. We apply this idea in the context of in silico protein structure prediction.
Research center :
Systèmes et Modélisation, GIGA-Research
Disciplines :
Computer science
Author, co-author :
Marcos Alvarez, Alejandro ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Maes, Francis ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Wehenkel, Louis ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Supervised learning to tune simulated annealing for in silico protein structure prediction
Publication date :
25 April 2012
Event name :
ESANN 2012, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P., Optimization by simulated annealing (1983) Science, New Series, 220 (4598), pp. 671-680
Koehl, P., Protein structure prediction (2010) Biomedical Applications of Biophysics, Volume 3 of Handbook of Modern Biophysics, pp. 1-34. , Humana Press
Leaver-Fay, A., Rosetta3: An object-oriented software suite for the simulation and design of macromolecules (2011) Computer Methods, Part C, Volume 487 of Methods in Enzymology, pp. 545-574. , Michael L. Johnson and Ludwig Brand, editors, Academic Press
Marcos Alvarez, A., (2011) Prédiction De Structures De macromol´ecules Par Apprentissage Automatique, , Master’s thesis, University of Li`ege, Faculty of Engineering
Berger, A.L., Pietra, V.J., Pietra, S.A., A maximum entropy approach to natural language processing (1996) Computational Linguistics, 22 (1), pp. 39-71
Larrañaga, P., Lozano, J.A., (2002) Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, , Springer, October
Jones, D.T., Protein secondary structure prediction based on position-specific scoring matrices (1999) Journal of Molecular Biology, 292 (2), pp. 195-202
