Book section Open Access

Network Aggregation to Enhance Results Derived from Multiple Analytics

Duroux Diane; Climente-González Héctor; Wienbrandt Lars; Van Steen Kristel


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="URL">https://zenodo.org/record/3929189</identifier>
  <creators>
    <creator>
      <creatorName>Duroux Diane</creatorName>
      <affiliation>BIO3 - GIGA-R Medical Genomics, University of Liège, Liège, Belgium;</affiliation>
    </creator>
    <creator>
      <creatorName>Climente-González Héctor</creatorName>
      <affiliation>Institut Curie, PSL Research University, F-75005 Paris, France; INSERM, U900, F-75005 Paris, France; MINES ParisTech, PSL Research University, CBIO-Centre for Computational Biology, F-75006 Paris, France;</affiliation>
    </creator>
    <creator>
      <creatorName>Wienbrandt Lars</creatorName>
      <affiliation>Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany</affiliation>
    </creator>
    <creator>
      <creatorName>Van Steen Kristel</creatorName>
      <affiliation>BIO3 - GIGA-R Medical Genomics, University of Liège, Liège, Belgium; WELBIO researcher, University of Liège, Liège, Belgium</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Network Aggregation to Enhance Results Derived from Multiple Analytics</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <dates>
    <date dateType="Issued">2020-05-29</date>
  </dates>
  <resourceType resourceTypeGeneral="BookChapter"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3929189</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1007/978-3-030-49161-1_12</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/mlfpm</relatedIdentifier>
  </relatedIdentifiers>
  <version>v1</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The more complex data are, the higher the number of possibilities to extract partial information from those data. These possibilities arise by adopting different analytic approaches. The heterogeneity among these approaches and in particular the heterogeneity in results they produce are challenging for follow-up studies, including replication, validation and translational studies. Furthermore, they complicate the interpretation of findings with wide-spread relevance. Here, we take the example of statistical epistasis networks derived from genome-wide association studies with single nucleotide polymorphisms as nodes. Even though we are only dealing with a single data type, the epistasis detection problem suffers from many pitfalls, such as the wide variety of analytic tools to detect them, each highlighting different aspects of epistasis and exhibiting different properties in maintaining false positive control. To reconcile different network views to the same problem, we considered 3 network aggregation methods and discussed their performance in the context of epistasis network aggregation. We furthermore applied a latent class method as best performer to real-life data on inflammatory bowel disease (IBD) and highlighted its benefits to increase our understanding about IBD underlying genetic architectures.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/813533/">813533</awardNumber>
      <awardTitle>Machine Learning Frontiers in Precision Medicine</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
69
50
views
downloads
Views 69
Downloads 50
Data volume 28.7 MB
Unique views 63
Unique downloads 48

Share

Cite as