Bioinformatics. The Machine Learning Approach (Relié)

  • Mit / Bradford

  • Paru le : 01/01/2001
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An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function... > Lire la suite
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An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding more than ever. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas in which there is a lot of data but little theory, as in molecular biology. The goal in machine learning is to extract useful information from a body by building good probabilistic models - and to automate the process as much as possible. Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. This book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology. This edition contains expanded coverage of probabilistic graphical models and the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.
    • Machine-learning foundations: the probabilistic framework
    • Probabilistic modeling and interference: examples
    • Machine learning algorithms
    • Neural networks: the theory
    • Neural networks: applications
    • Hidden Markov models: the theory
    • Hidden Markov models: Applications
    • Probabilistic graphical models in bioinformatics
    • Probabilistic models of evolution: phylogenetic trees
    • Stochastic grammars and linguistics
    • Microarrays and gene expression
    • Internet resources and public databases.
  • Date de parution : 01/01/2001
  • Editeur : Mit / Bradford
  • ISBN : 0-262-02506-X
  • EAN : 9780262025065
  • Présentation : Relié
  • Nb. de pages : 452 pages
  • Poids : 1.105 Kg
  • Dimensions : 18,5 cm × 23,6 cm × 3,2 cm
Pierre Baldi is Professor and Director of the Institute for Genomics and Bioinformatics in the Department of Information and Computer Science and in the Department of Biological Chemistry in the College of Medicine at the University of California, Irvine. Soren Brunak is Professor and Director of the Center for Biological Sequence Analysis at the Biocentrum of the Technical University of Denmark.

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Bioinformatics. The Machine Learning Approach
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