000 03953nam a2200661 i 4500
001 6267217
003 IEEE
005 20220712204601.0
006 m o d
007 cr |n|||||||||
008 151223s2001 maua ob 001 eng d
020 _a9780262255707
_qebook
020 _z0262255707
_qelelelectronic
020 _z9780262025065
_qprint
035 _a(CaBNVSL)mat06267217
035 _a(IDAMS)0b000064818b419b
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aQH506
_b.B35 2001eb
060 4 _aQH 506
_bB177b 2001
082 0 4 _a572.8/01/13
_221
100 1 _aBaldi, Pierre,
_eauthor.
_921560
245 1 0 _aBioinformatics :
_bthe machine learning approach /
_cPierre Baldi, Sren Brunak.
250 _a2nd ed.
264 1 _aCambridge, Massachusetts :
_bMIT Press,
_cc2001.
264 2 _a[Piscataqay, New Jersey] :
_bIEEE Xplore,
_c[2001]
300 _a1 PDF (xxi, 452 pages) :
_billustrations.
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _aAdaptive computation and machine learning series
500 _a"A Bradford book."
504 _aIncludes bibliographical references.
506 1 _aRestricted to subscribers or individual electronic text purchasers.
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.
530 _aAlso available in print.
538 _aMode of access: World Wide Web
550 _aMade available online by Ebrary.
588 _aDescription based on PDF viewed 12/23/2015.
650 0 _aBioinformatics.
_99561
650 0 _aMolecular biology
_xComputer simulation.
_921561
650 0 _aMolecular biology
_xMathematical models.
_921562
650 0 _aNeural networks (Computer science)
_93414
650 0 _aMachine learning.
_91831
650 0 _aMarkov processes.
_98309
650 1 2 _aComputational Biology
_xmethods.
_921563
650 2 2 _aArtificial Intelligence.
_93407
650 2 2 _aMarkov Chains.
_921564
650 2 2 _aModels, Theoretical.
_921565
650 2 2 _aNeural Networks (Computer)
_921566
655 0 _aElectronic books.
_93294
700 1 _aBrunak, Sren.
_921567
710 2 _aIEEE Xplore (Online Service),
_edistributor.
_921568
710 2 _aMIT Press,
_epublisher.
_921569
776 0 8 _iPrint version
_z9780262025065
830 0 _aAdaptive computation and machine learning
_921570
856 4 2 _3Abstract with links to resource
_uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267217
942 _cEBK
999 _c72875
_d72875