MARC Record from marc_columbia

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001 4240444
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060 4 $aQH 506$bB177b 2001
072 7 $aSCI$x049000$2bisacsh
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082 04 $a572.8/01/13$221
049 $aZCUA
100 1 $aBaldi, Pierre.
245 10 $aBioinformatics :$bthe machine learning approach /$cPierre Baldi, Søren Brunak.
250 $a2nd ed.
260 $aCambridge, Mass. :$bMIT Press,$c©2001.
300 $a1 online resource (xxi, 452 pages) :$billustrations
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
347 $adata file
490 1 $aAdaptive computation and machine learning
500 $a"A Bradford book."
504 $aIncludes bibliographical references (pages 409-445).
505 0 $aIntroduction -- Machine-learning foundations : the probabilistic framework -- Probabilistic modeling and inference : 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 : phylogenitc trees -- Stochastic grammars and linguistics -- Microarrays and gene expression -- Internet resources and public databases.
588 0 $aPrint version record.
520 $aAn 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 rapidly. 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 where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible. In this book 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. The 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 new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised.
546 $aEnglish.
650 0 $aBioinformatics.
650 0 $aMolecular biology$xComputer simulation.
650 0 $aMolecular biology$xMathematical models.
650 0 $aNeural networks (Computer science)
650 0 $aMachine learning.
650 0 $aMarkov processes.
650 2 $aArtificial Intelligence
650 2 $aComputational Biology$xmethods
650 2 $aMarkov Chains
650 2 $aModels, Theoretical
650 2 $aNeural Networks, Computer.
650 6 $aBio-informatique.
650 6 $aBiologie moléculaire$xSimulation par ordinateur.
650 6 $aBiologie moléculaire$xModèles mathématiques.
650 6 $aRéseaux neuronaux (Informatique)
650 6 $aApprentissage automatique.
650 6 $aProcessus de Markov.
650 7 $aSCIENCE$xLife Sciences$xMolecular Biology.$2bisacsh
650 7 $aBioinformatics.$2fast$0(OCoLC)fst00832181
650 7 $aMachine learning.$2fast$0(OCoLC)fst01004795
650 7 $aMarkov processes.$2fast$0(OCoLC)fst01010347
650 7 $aMolecular biology$xComputer simulation.$2fast$0(OCoLC)fst01024737
650 7 $aMolecular biology$xMathematical models.$2fast$0(OCoLC)fst01024743
650 7 $aNeural networks (Computer science)$2fast$0(OCoLC)fst01036260
655 0 $aElectronic books.
655 4 $aElectronic books.
700 1 $aBrunak, Søren.
776 08 $iPrint version:$aBaldi, Pierre.$tBioinformatics.$b2nd ed.$dCambridge, Mass. : MIT Press, ©2001$z026202506X$w(DLC) 2001030210$w(OCoLC)45951728
830 0 $aAdaptive computation and machine learning.
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio4240444$zAll EBSCO eBooks
852 8 $blweb$hEBOOKS