LEADER 04720nam 2200793 450 001 9910779823603321 005 20230125185411.0 010 $a0-262-30740-5 010 $a1-282-09608-7 010 $a9786612096082 010 $a0-262-25570-7 010 $a0-585-44466-8 035 $a(CKB)111056485409358 035 $a(EBL)3338802 035 $a(SSID)ssj0000111873 035 $a(PQKBManifestationID)11131000 035 $a(PQKBTitleCode)TC0000111873 035 $a(PQKBWorkID)10080369 035 $a(PQKB)10627303 035 $a(CaBNVSL)mat06267217 035 $a(IDAMS)0b000064818b419b 035 $a(IEEE)6267217 035 $a(Au-PeEL)EBL3338802 035 $a(CaPaEBR)ebr10225255 035 $a(OCoLC)815776271 035 $a(MiAaPQ)EBC3338802 035 $a(PPN)258657006 035 $a(EXLCZ)99111056485409358 100 $a20151223d2001 uy 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBioinformatics $ethe machine learning approach /$fPierre Baldi, Sren Brunak 205 $a2nd ed. 210 1$aCambridge, Massachusetts :$cMIT Press,$dc2001. 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2001] 215 $a1 online resource (477 p.) 225 1 $aAdaptive computation and machine learning series 300 $a"A Bradford book." 311 $a0-262-02506-X 320 $aIncludes bibliographical references. 327 $aContents; Series Foreword; Preface; 1 Introduction; 2 Machine-Learning Foundations: The Probabilistic Framework; 3 Probabilistic Modeling and Inference: Examples; 4 Machine Learning Algorithms; 5 Neural Networks: The Theory; 6 Neural Networks: Applications; 7 Hidden Markov Models: The Theory; 8 Hidden Markov Models: Applications; 9 Probabilistic Graphical Models in Bioinformatics; 10 Probabilistic Models of Evolution: Phylogenetic Trees; 11 Stochastic Grammars and Linguistics; 12 Microarrays and Gene Expression; 13 Internet Resources and Public Databases; A Statistics 327 $aB Information Theory, Entropy, and Relative EntropyC Probabilistic Graphical Models; D HMM Technicalities, Scaling, Periodic Architectures, State Functions, and Dirichlet Mixtures; E Gaussian Processes, Kernel Methods, and Support Vector Machines; F Symbols and Abbreviations; References; Index 330 $aAn unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Soren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised. 410 0$aAdaptive computation and machine learning 606 $aBioinformatics 606 $aMolecular biology$xComputer simulation 606 $aMolecular biology$xMathematical models 606 $aNeural networks (Computer science) 606 $aMachine learning 606 $aMarkov processes 615 0$aBioinformatics. 615 0$aMolecular biology$xComputer simulation. 615 0$aMolecular biology$xMathematical models. 615 0$aNeural networks (Computer science) 615 0$aMachine learning. 615 0$aMarkov processes. 676 $a572.8 700 $aBaldi$b Pierre$0282482 701 $aBrunak$b Sren$01553222 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910779823603321 996 $aBioinformatics$93813610 997 $aUNINA