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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

11th European Conference, EvoBIO 2013, Vienna, Austria, April 3-5, 2013, Proceedings

This book constitutes the refereed proceedings of the 11th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2013, held in Vienna, Austria, in April 2013, colocated with the Evo* 2013 events EuroGP, EvoCOP, EvoMUSART and EvoApplications. The 10 revised full papers presented together with 9 poster papers were carefully reviewed and selected from numerous submissions. The papers cover a wide range of topics in the field of biological data analysis and computational biology. They address important problems in biology, from the molecular and genomic dimension to the individual and population level, often drawing inspiration from biological systems in oder to produce solutions to biological problems.

The task of predicting protein functions using computational techniques is a
major research area in the field of bioinformatics. Casting the task into a
classification problem makes it challenging, since the classes (functions) to be
predicted are ...

Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics

10th European Conference, EvoBIO 2012, Málaga, Spain, April 11-13, 2012, Proceedings

This book constitutes the refereed proceedings of the 10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2012, held in Málaga, Spain, in April 2012 co-located with the Evo* 2012 events. The 15 revised full papers presented together with 8 poster papers were carefully reviewed and selected from numerous submissions. Computational Biology is a wide and varied discipline, incorporating aspects of statistical analysis, data structure and algorithm design, machine learning, and mathematical modeling toward the processing and improved understanding of biological data. Experimentalists now routinely generate new information on such a massive scale that the techniques of computer science are needed to establish any meaningful result. As a consequence, biologists now face the challenges of algorithmic complexity and tractability, and combinatorial explosion when conducting even basic analyses.

Inferring Disease-Related Metabolite Dependencies with a Bayesian
Optimization Algorithm Holger Franken1, Alexander Seitz1, Rainer Lehmann2,3,
Hans-Ulrich Häring2,3, Norbert Stefan2,3, and Andreas Zell1 1 Center for
Bioinformatics ...