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We present an ML-based methodology for\n transcriptome analysis via comparison of gene coexpression networks,\n implemented as an R package called machine learning–based differential\n network analysis (mlDNA) and apply this method to reanalyze a set of\n abiotic stress expression data in Arabidopsis thaliana. The mlDNA first\n used a ML-based filtering process to remove nonexpressed, constitutively\n expressed, or non-stress-responsive “noninformative” genes prior to\n network construction, through learning the patterns of 32 expression\n characteristics of known stress-related genes. The retained “informative”\n genes were subsequently analyzed by ML-based network comparison to predict\n candidate stress-related genes showing expression and network differences\n between control and stress networks, based on 33 network topological\n characteristics. Comparative evaluation of the network-centric and\n gene-centric analytic methods showed that mlDNA substantially outperformed\n traditional statistical testing–based differential expression analysis at\n identifying stress-related genes, with markedly improved prediction\n accuracy. To experimentally validate the mlDNA predictions, we selected 89\n candidates out of the 1784 predicted salt stress–related genes with\n available SALK T-DNA mutagenesis lines for phenotypic screening and\n identified two previously unreported genes, mutants of which showed\n salt-sensitive phenotypes.","descriptionType":"Abstract"},{"description":"Supplemental Dataset\n 1Known Stress-related\n Genes Collected from the TAIR and DRASTIC Databases, their Expression\n Changes in the Stress Microarray Datasets, and the Statistical Results of\n their Gene Ontology (GO) AnnotationsSupplemental Dataset\n 2“Informative” Genes\n Obtained from PSOL-based ML Analysis for Gene Co-expression Network\n Construction under Six Studied Stresses in Two TissuesSupplemental Dataset\n 3Candidate Stress-related\n Genes Predicted by mlDNASupplemental Dataset\n 4List\n of the Candidate Stress-related Genes Evidenced by a High-throughput\n Phenotypic Screening ExperimentSupplemental Dataset\n 5Detailed Information for\n Gene Ontology (GO) Modules Enriched with Salt Stress-related\n GenesSupplemental Dataset\n 6List\n of Stress Shared GenesSupplemental Dataset\n 7List\n of Stress-Specific Genes","descriptionType":"Other"}],"geoLocations":[{"geoLocationPlace":"USA"},{"geoLocationPlace":"Tucson"},{"geoLocationPlace":"Arizona"}],"fundingReferences":[],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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  <identifier identifierType="DOI">10.5061/DRYAD.41B9G</identifier>
  <creators>
    <creator>
      <creatorName nameType="Personal">Ma, Chuang</creatorName>
      <affiliation affiliationIdentifier="https://ror.org/03m2x1q45" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">University of Arizona</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Xin, Mingming</creatorName>
      <affiliation affiliationIdentifier="https://ror.org/03m2x1q45" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">University of Arizona</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Feldmann, Kenneth A.</creatorName>
      <affiliation affiliationIdentifier="https://ror.org/03m2x1q45" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">University of Arizona</affiliation>
    </creator>
    <creator>
      <creatorName nameType="Personal">Wang, Xiangfeng</creatorName>
      <affiliation affiliationIdentifier="https://ror.org/03m2x1q45" affiliationIdentifierScheme="ROR" schemeURI="https://ror.org">University of Arizona</affiliation>
    </creator>
  </creators>
  <titles>
    <title>
      Data from: Machine learning-based differential network analysis: a study
      of stress-responsive transcriptomes in Arabidopsis thaliana
    </title>
  </titles>
  <publisher publisherIdentifier="https://ror.org/00x6h5n95" publisherIdentifierScheme="ROR" schemeURI="https://ror.org/">Dryad</publisher>
  <resourceType resourceTypeGeneral="Dataset">dataset</resourceType>
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    <subject subjectScheme="PLOS Subject Area Thesaurus" schemeURI="https://github.com/PLOS/plos-thesaurus">Arabidopsis thaliana</subject>
    <subject>abiotic/environmental stress</subject>
    <subject>differential network</subject>
    <subject subjectScheme="PLOS Subject Area Thesaurus" schemeURI="https://github.com/PLOS/plos-thesaurus">Bioinformatics</subject>
    <subject subjectScheme="PLOS Subject Area Thesaurus" schemeURI="https://github.com/PLOS/plos-thesaurus">Transcriptome analysis</subject>
    <subject>gene coexpression network</subject>
  </subjects>
  <dates>
    <date dateType="Submitted">2014-01-07T04:52:01Z</date>
    <date dateType="Issued">2015-01-07T00:00:00Z</date>
    <date dateType="Available">2015-01-07T00:00:00Z</date>
  </dates>
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    <description descriptionType="Abstract">
      Machine learning (ML) is an intelligent data mining technique that builds
      a prediction model based on the learning of prior knowledge to recognize
      patterns in large-scale data sets. We present an ML-based methodology for
      transcriptome analysis via comparison of gene coexpression networks,
      implemented as an R package called machine learning–based differential
      network analysis (mlDNA) and apply this method to reanalyze a set of
      abiotic stress expression data in Arabidopsis thaliana. The mlDNA first
      used a ML-based filtering process to remove nonexpressed, constitutively
      expressed, or non-stress-responsive “noninformative” genes prior to
      network construction, through learning the patterns of 32 expression
      characteristics of known stress-related genes. The retained “informative”
      genes were subsequently analyzed by ML-based network comparison to predict
      candidate stress-related genes showing expression and network differences
      between control and stress networks, based on 33 network topological
      characteristics. Comparative evaluation of the network-centric and
      gene-centric analytic methods showed that mlDNA substantially outperformed
      traditional statistical testing–based differential expression analysis at
      identifying stress-related genes, with markedly improved prediction
      accuracy. To experimentally validate the mlDNA predictions, we selected 89
      candidates out of the 1784 predicted salt stress–related genes with
      available SALK T-DNA mutagenesis lines for phenotypic screening and
      identified two previously unreported genes, mutants of which showed
      salt-sensitive phenotypes.
    </description>
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      &lt;div class="o-metadata__file-usage-entry"&gt;&lt;h4
      class="o-heading__level3-file-title"&gt;Supplemental Dataset
      1&lt;/h4&gt;&lt;div
      class="o-metadata__file-description"&gt;Known Stress-related
      Genes Collected from the TAIR and DRASTIC Databases, their Expression
      Changes in the Stress Microarray Datasets, and the Statistical Results of
      their Gene Ontology (GO) Annotations&lt;/div&gt;&lt;div
      class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div
      class="o-metadata__file-usage-entry"&gt;&lt;h4
      class="o-heading__level3-file-title"&gt;Supplemental Dataset
      2&lt;/h4&gt;&lt;div
      class="o-metadata__file-description"&gt;“Informative” Genes
      Obtained from PSOL-based ML Analysis for Gene Co-expression Network
      Construction under Six Studied Stresses in Two Tissues&lt;/div&gt;&lt;div
      class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div
      class="o-metadata__file-usage-entry"&gt;&lt;h4
      class="o-heading__level3-file-title"&gt;Supplemental Dataset
      3&lt;/h4&gt;&lt;div
      class="o-metadata__file-description"&gt;Candidate Stress-related
      Genes Predicted by mlDNA&lt;/div&gt;&lt;div
      class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div
      class="o-metadata__file-usage-entry"&gt;&lt;h4
      class="o-heading__level3-file-title"&gt;Supplemental Dataset
      4&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;List
      of the Candidate Stress-related Genes Evidenced by a High-throughput
      Phenotypic Screening Experiment&lt;/div&gt;&lt;div
      class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div
      class="o-metadata__file-usage-entry"&gt;&lt;h4
      class="o-heading__level3-file-title"&gt;Supplemental Dataset
      5&lt;/h4&gt;&lt;div
      class="o-metadata__file-description"&gt;Detailed Information for
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      Genes&lt;/div&gt;&lt;div
      class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div
      class="o-metadata__file-usage-entry"&gt;&lt;h4
      class="o-heading__level3-file-title"&gt;Supplemental Dataset
      6&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;List
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      class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;&lt;div
      class="o-metadata__file-usage-entry"&gt;&lt;h4
      class="o-heading__level3-file-title"&gt;Supplemental Dataset
      7&lt;/h4&gt;&lt;div class="o-metadata__file-description"&gt;List
      of Stress-Specific Genes&lt;/div&gt;&lt;div
      class="o-metadata__file-name"&gt;&lt;/div&gt;&lt;/div&gt;
    </description>
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