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  <titleInfo>
    <title>Statistical diagnostics for cancer : analyzing high-dimensional data</title>
  </titleInfo>
  <name type="personal">
    <namePart>Emmert-Streib, Frank</namePart>
    <role>
      <roleTerm type="text">editor.</roleTerm>
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  </name>
  <name type="personal">
    <namePart>Dehmer, Matthias</namePart>
    <namePart type="date">1968-</namePart>
    <role>
      <roleTerm type="text">editor.</roleTerm>
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  <genre authority="marc">bibliography</genre>
  <genre authority="">Electronic books.</genre>
  <genre authority="local">Electronic books.</genre>
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    <dateIssued encoding="marc">2013</dateIssued>
    <copyrightDate encoding="marc">2013</copyrightDate>
    <edition>First edition.</edition>
    <issuance>monographic</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <form authority="gmd">electronic resource</form>
    <extent>1 online resource (xx, 292 pages) : illustrations (some color).</extent>
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  <abstract>This title discusses different methods for statistically analyzing and validating data created with high-throughput methods. It focuses on systems approaches, meaning that no single gene or protein forms the basis of the analysis but rather a more or less complex biological network.</abstract>
  <tableOfContents>Part one: General overview. Control of type I error rates for oncology biomarker discovery with high-throughput platforms -- Overview of public cancer databases, resources, and visualization tools -- Part two: Bayesian methods. Discovery of expression signatures in chronic myeloid leukemia by Bayesian model averaging -- Bayesian ranking and selection methods in microarray studies -- Multiclass classification via Bayesian variable selection with gene expression data -- Semisupervised methods for analyzing high-dimensional genomic data -- Part three: Network-based approaches -- Colorectal cancer and its molecular subsystems: construction, interpretation, and validation -- Network medicine: disease genes in molecular networks -- Inference of gene regulatory networks in breast and ovarian cancer by integrating different genomic data -- Network-module-based approaches in cancer data analysis -- Discriminant and network analysis to study origin of cancer -- Intervention and control of gene regulatory networks: theoretical framework and application to human melanoma gene regulation -- Part four: Phenotype influence of DNA copy number aberrations. Identification of recurrent DNA copy number aberrations in tumors -- The cancer cell, its entropy, and high-dimensional molecular data.</tableOfContents>
  <note type="statement of responsibility">edited by Frank Emmert-Streib and Matthias Dehmer.</note>
  <note>Edition statement from running title area.</note>
  <note>Includes bibliographical references and index.</note>
  <subject authority="lcsh">
    <topic>Cancer</topic>
    <topic>Diagnosis</topic>
  </subject>
  <subject authority="mesh">
    <topic>Neoplasms</topic>
    <topic>genetics</topic>
  </subject>
  <subject authority="mesh">
    <topic>Statistics as Topic</topic>
    <topic>methods</topic>
  </subject>
  <subject authority="fast">
    <topic>Cancer</topic>
    <topic>Diagnosis</topic>
  </subject>
  <classification authority="lcc">RC270 .S73 2013eb</classification>
  <classification authority="ddc" edition="23">616.99/4075</classification>
  <classification authority="nlm">QZ 241</classification>
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    <titleInfo>
      <title>Statistical diagnostics for cancer</title>
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    <originInfo>
      <publisher>Weinheim, germany : Wiley-Blackwell, [2013]</publisher>
    </originInfo>
    <identifier type="local">(OCoLC)840878109</identifier>
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    <titleInfo>
      <title>Quantitative and network biology ; v. 3</title>
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  <identifier type="stock number">447101 MIL</identifier>
  <identifier type="uri">http://onlinelibrary.wiley.com/book/10.1002/9783527665471</identifier>
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    <recordIdentifier source="OCoLC">ocn834600291</recordIdentifier>
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