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Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics [electronic resource] : 10th European Conference, EvoBIO 2012, Málaga, Spain, April 11-13, 2012. Proceedings / edited by Mario Giacobini, Leonardo Vanneschi, William S. Bush.

Contributor(s): Material type: TextTextSeries: Lecture Notes in Computer Science ; 7246Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2012Description: XIII, 255p. 76 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783642290664
Subject(s): Additional physical formats: Printed edition:: No titleDDC classification:
  • 570.285 23
LOC classification:
  • QH324.2-324.25
Online resources: In: Springer eBooksSummary: 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.
Item type: E-BOOKS
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IMSc Library Link to resource Available EBK10174

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.

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