| Larrañaga P, Calvo B, Santana R, Bielza C, Galdiano J, Inza I, Lozano JA, Armañanzas R, Santafé G, Pérez A and Robles V (2006), "Machine learning in bioinformatics", Briefings in Bioinformatics. Vol. 7, pp. 86-112.
[Abstract] [BibTeX] [URL]
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| Abstract: This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown. |
BibTeX:
@article{Larranhaga_et_al:2005,
author = {P. Larrañaga and B. Calvo and R. Santana and C. Bielza and J. Galdiano and I. Inza and J. A. Lozano and R. Armañanzas and G. Santafé and A. Pérez and V. Robles},
title = {Machine learning in bioinformatics},
journal = {Briefings in Bioinformatics},
year = {2006},
volume = {7},
pages = {86--112},
url = {http://dx.doi.org/10.1093/bib/bbk007}
}
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| Santana R, Larrañaga P and Lozano JA (2006), "Mixtures of Kikuchi approximations", In Proceedings of the 17th European Conference on Machine Learning: ECML 2006. Vol. 4212, pp. 365-376. Springer.
[Abstract] [BibTeX] [URL]
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| Abstract: Mixtures of distributions concern modeling a probability distribution by a weighted sum of other distributions. Kikuchi approximations of probability distributions follow an approach to approximate the free energy of statistical systems. In this paper, we introduce the mixture of Kikuchi approximations as a probability model. We present an algorithm for learning Kikuchi approximations from data based on the expectation-maximization (EM) paradigm. The proposal is tested in the approximation of probability distributions that arise in evolutionary computation. |
BibTeX:
@inproceedings{Santana_et_al:2006a,
author = {R. Santana and Pedro Larrañaga and J. A. Lozano},
editor = {Johannes Fürnkranz and Tobias Scheffer and Myra Spiliopoulou},
title = {Mixtures of Kikuchi approximations},
booktitle = {Proceedings of the 17th European Conference on Machine Learning: ECML 2006},
publisher = {Springer},
year = {2006},
volume = {4212},
pages = {365-376},
url = {http://dx.doi.org/10.1007/11871842_36}
}
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| Santana R (2006), "Advances in Probabilistic Graphical Models for Optimization and Learning. Applications in Protein Modelling". Thesis at: University of the Basque Country.
[Abstract] [BibTeX]
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| Abstract: The first part focuses on the analysis of undirected graphical models. The ways in which inference and sampling methods based on undirected graphical models can be applied to optimization are discussed. The second part of the thesis introduces a number of properties of the class of Kikuchi approximation that use clique-based decompositions. An algorithm that learns this approximation from data is introduced and evaluated in different types of approximation problem. Region-based decompositions in optimization methods that use inference techniques are analyzed. The second part comprises three chapters. The third part of the thesis addresses the application of optimization algorithms based on graphical models to problems from computational biology. It starts by reviewing a number of computational protein problems. Different proposals that allow the efficient solution of some of these problems are introduced. A link to the results achieved in the first part of the thesis is presented by showing how the probabilistic models and techniques introduced can be added to obtain solutions good enough for these problems. Part four consists of only one chapter that presents the conclusions of the thesis. |
BibTeX:
@phdthesis{Santana:2006,
author = {R. Santana},
title = {Advances in Probabilistic Graphical Models for Optimization and Learning. Applications in Protein Modelling},
school = {University of the Basque Country},
year = {2006}
}
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