| Alvarez-Ginarte YM, Crespo R, Montero-Cabrera LA, Ruiz-Garcia JA, Ponce YM, Santana R, Pardillo-Fontdevila E and Alonso-Becerra E (2005), "A novel in-silico approach for QSAR Studies of Anabolic and Androgenic Activities in the 17-hydroxy-5-androstane Steroid Family", QSAR & Combinatorial Science. Vol. 24, pp. 218-226. |
| Abstract: Predictive Quantitative Structure-Activity Relationship (QSAR) models of anabolic and androgenic activities for the 17β-hydroxy-5α-androstane steroid family were obtained by means of multi-linear regression using quantum and physicochemical molecular descriptors and a genetic algorithm for the selection of the best set of descriptors. The model allows the identification, selection and future design of new steroid molecules with increased anabolic activity. Molecular descriptors included in reported models allow the structural interpretation of the biological process, evidencing the main role of the shape of molecules, hydrophobicity and electronic properties. The model for the anabolic/androgenic ratio (expressed by the weight of the levator ani muscle and ventral prostate in mice) predicts that: a) 2-cyano-17-α-methyl-17-β-acetoxy-5α-androst-2-ene is the most potent anabolic steroid in the group and b) the testosterone-3-cyclopentenyl-enoleter is the less potent one. The approach described in this paper is an alternative for the discovery and optimization of leading anabolic compounds. |
BibTeX:
@article{Alvarez-Ginarte_et_al:2005,
author = {Yoanna María Alvarez-Ginarte and Rachel Crespo and Luis Alberto Montero-Cabrera and José Alberto Ruiz-Garcia and Yovani Marrero Ponce and Roberto Santana and Eladio Pardillo-Fontdevila and Esther Alonso-Becerra},
title = {A novel in-silico approach for QSAR Studies of Anabolic and Androgenic Activities in the 17-hydroxy-5-androstane Steroid Family},
journal = {QSAR & Combinatorial Science},
year = {2005},
volume = {24},
pages = {218--226},
url = {http://dx.doi.org/10.1002/qsar.200430889}
}
|
| Santana R, Larrañaga P and Lozano JA (2005), "Properties of Kikuchi approximations constructed from clique based decompositions". Research Report at: Department of Computer Science and Artificial Intelligence, University of the Basque Country., April, 2005. (EHU-KZAA-IK-2/05) |
| Abstract: Kikuchi approximations constructed from clique-based decompositions can be used to calculate suitable approximations of probability distributions. They can be applied in domains such as probabilistic modeling, supervised and unsupervised classi cation, and evolutionary algorithms. This paper introduces a number of properties of these approximations. Pairwise and local Markov properties of the Kikuchi approximations are proved. We prove that, even if the global Markov property is not satisfied in the general case, it is possible to decompose the Kikuchi approximation in the product of local Kikuchi approximations defined on a decomposition of the graph. Partial Kikuchi approximations are introduced. Additionally, the paper clarifies the place of clique-based decompositions in relation to other techniques inspired by methods from statistical physics, and discusses the application of the results introduced in the paper for the conception of Kikuchi approximation learning algorithms. |
BibTeX:
@techreport{Santana_et_al:2005a,
author = {R. Santana and P. Larrañaga and J. A. Lozano},
title = {Properties of Kikuchi approximations constructed from clique based decompositions},
school = {Department of Computer Science and Artificial Intelligence, University of the Basque Country},
year = {2005},
number = {EHU-KZAA-IK-2/05},
url = {http://www.sc.ehu.es/ccwbayes/members/rsantana/papers/TechReports/ResearchRepProperties.pdf}
}
|
| Santana R, Larrañaga P and Lozano JA (2005), "Interactions and dependencies in estimation of distribution algorithms", In Proceedings of the 2005 Congress on Evolutionary Computation CEC-2005. Edinburgh, U.K. , pp. 1418-1425. IEEE Press. |
| Abstract: In this paper, we investigate two issues related to probabilistic modeling in estimation of distribution algorithms (EDAs). First, we analyze the effect of selection in the arousal of probability dependencies in EDAs for random functions. We show that, for these functions, independence relationships not represented by the function structure are likely to appear in the probability model. Second, we propose an approach to approximate probability distributions in EDAs using a subset of the dependencies that exist in the data. An EDA that employs only malign interactions is introduced. Preliminary experiments presented show how the probability approximations based solely on malign interactions, can be applied to EDAs. |
BibTeX:
@inproceedings{Santana_et_al:2005b,
author = {R. Santana and P. Larrañaga and J. A. Lozano},
title = {Interactions and dependencies in estimation of distribution algorithms},
booktitle = {Proceedings of the 2005 Congress on Evolutionary Computation CEC-2005},
publisher = {IEEE Press},
year = {2005},
pages = {1418--1425},
url = {http://dx.doi.org/10.1109/CEC.2005.1554856}
}
|
| Santana R, Larrañaga P and Lozano JA (2005), "Aprendizaje y muestreo de la aproximación Kikuchi", In Proceedings of the III Taller Nacional de Minería de Datos y Aprendizaje (TAMIDA-2005). Granada, Spain , pp. 97-105. Thomson. |
| Abstract: En este trabajo se presentan un algoritmo para el aprendizaje de la aproximación Kikuchi a partir de datos así como un método de muestreo de la referida aproximación. Se discuten resultados preliminares de la evaluación del algoritmo en el aprendizaje en datos generados a partir de una instancia del modelo Ising de ferromagnetismo. Los resultados obtenidos indican que la aproximación Kikuchi es capaz de representar de manera factible las dependencias existentes en los datos entre las distintas variables. |
BibTeX:
@inproceedings{Santana_et_al:2005c,
author = {R. Santana and P. Larrañaga and J. A. Lozano},
title = {Aprendizaje y muestreo de la aproximación Kikuchi},
booktitle = {Proceedings of the III Taller Nacional de Minería de Datos y Aprendizaje (TAMIDA-2005)},
publisher = {Thomson},
year = {2005},
pages = {97--105},
url = {http://www.lsi.us.es/redmidas/CEDI/papers/501.pdf}
}
|
| Santana R, Larrañaga P and Lozano JA (2005), "Protein structure prediction in simplified models with estimation of distribution algorithms", In Proceedings of the IV Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB-2005). Granada, Spain , pp. 245-252. Thomson. |
| Abstract: In this paper we discuss the use of probabilistic modeling in the solution of the protein structure prediction problem. Estimation of distribution algorithms (EDAs) based on Markov models are presented as an alternative to other nature-inspired optimization algorithms for the solution of protein simplified models. |
BibTeX:
@inproceedings{Santana_et_al:2005d,
author = {R. Santana and P. Larrañaga and J. A. Lozano},
title = {Protein structure prediction in simplified models with estimation of distribution algorithms},
booktitle = {Proceedings of the IV Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB-2005)},
publisher = {Thomson},
year = {2005},
pages = {245--252}
}
|
| Santana R (2005), "Estimation of distribution algorithms with Kikuchi approximations", Evolutionary Computation. Vol. 13(1), pp. 67-97. |
| Abstract: The question of finding feasible ways for estimating probability distributions is one of the main challenges for Estimation of Distribution Algorithms (EDAs). To estimate the distribution of the selected solutions, EDAs use factorizations constructed according to graphical models. The class of factorizations that can be obtained from these probability models is highly constrained. Expanding the class of factorizations that could be employed for probability approximation is a necessary step for the conception of more robust EDAs. In this paper we introduce a method for learning a more general class of probability factorizations. The method combines a reformulation of a probability approximation procedure known in statistical physics as the Kikuchi approximation of energy, with a novel approach for finding graph decompositions. We present the Markov Network Estimation of Distribution Algorithm (MN-EDA), an EDA that uses Kikuchi approximations to estimate the distribution, and Gibbs Sampling (GS) to generate new points. A systematic empirical evaluation of MN-EDA is done in comparison with different Bayesian network based EDAs. From our experiments we conclude that the algorithm can outperform other EDAs that use traditional methods of probability approximation in the optimization of functions with strong interactions among their variables. |
BibTeX:
@article{Santana:2005,
author = {R. Santana},
title = {Estimation of distribution algorithms with Kikuchi approximations},
journal = {Evolutionary Computation},
year = {2005},
volume = {13},
number = {1},
pages = {67--97},
url = {http://www.mitpressjournals.org/doi/abs/10.1162/1063656053583496}
}
|