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Atlas of Cancer Signaling Network: A resource of multi-scale biological maps to study disease mechanisms

L Cristobal Monraz Gomez; Maria Kondratova; Nicolas Sompairac; Christine Lonjou; Jean-Marie Ravel; Emmanuel Barillot; Andrei Zinovyev; Inna Kuperstein


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  <identifier identifierType="URL">https://zenodo.org/record/3896747</identifier>
  <creators>
    <creator>
      <creatorName>L Cristobal Monraz Gomez</creatorName>
      <affiliation>Institut Curie</affiliation>
    </creator>
    <creator>
      <creatorName>Maria Kondratova</creatorName>
      <affiliation>Institut Curie</affiliation>
    </creator>
    <creator>
      <creatorName>Nicolas Sompairac</creatorName>
      <affiliation>Institut Curie</affiliation>
    </creator>
    <creator>
      <creatorName>Christine Lonjou</creatorName>
      <affiliation>Institut Curie</affiliation>
    </creator>
    <creator>
      <creatorName>Jean-Marie Ravel</creatorName>
      <affiliation>Institut Curie</affiliation>
    </creator>
    <creator>
      <creatorName>Emmanuel Barillot</creatorName>
      <affiliation>Institut Curie</affiliation>
    </creator>
    <creator>
      <creatorName>Andrei Zinovyev</creatorName>
      <affiliation>Institut Curie</affiliation>
    </creator>
    <creator>
      <creatorName>Inna Kuperstein</creatorName>
      <affiliation>Institut Curie</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Atlas of Cancer Signaling Network: A resource of multi-scale biological maps to study disease mechanisms</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Biocuration</subject>
    <subject>Cancer</subject>
    <subject>Data visualization</subject>
    <subject>Knowledge formalization</subject>
    <subject>Modelling, Molecular network</subject>
    <subject>Signaling network map</subject>
    <subject>Systems biology</subject>
    <subject>Systems medicine</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-06-01</date>
  </dates>
  <resourceType resourceTypeGeneral="BookChapter"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3896747</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1016/B978-0-12-801238-3.11683-6</relatedIdentifier>
  </relatedIdentifiers>
  <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;ACSN (https://acsn.curie.fr) is a web-based resource of multi-scale biological maps depicting molecular processes in cancer cell and tumor microenvironment. The core of the Atlas is a set of interconnected cancer-related signaling and metabolic network maps. Molecular mechanisms are depicted on the maps at the level of biochemical interactions, forming a large seamless network. The Atlas is a &amp;quot;geographic-like&amp;quot; interactive &amp;quot;world map&amp;quot; of molecular interactions leading the hallmarks of cancer as described by Hanahan and Weinberg. The Atlas is created using the systems biology standards and therefore is amenable for computational analysis. The maps of ACSN are organized in a hierarchical manner and decomposed into functional modules with meaningful network layout. Navigation of the ACSN is intuitive thanks to the Google Maps-like features in the NaviCell web platform. Particularly, the exploration of the Atlas is simplified due to the semantic zooming feature, allowing the user to visualize the seamless Atlas and individual maps from the collection at different levels of details. The resource includes tools for visualization and analysis of molecular data in the context of signaling network maps. In this chapter we present how a disease-specific resource such as the&lt;br&gt;
Atlas of Cancer Signaling Network (ACSN) can be useful to study and interpret molecular perturbations in cancer, among others, explaining drug resistance, suggesting intervention points, finding phenotype shifts through disease progression, explaining susceptibility to a particular type of cancer and studying disease comorbidities.&lt;/p&gt;</description>
    <description descriptionType="Other">This is an author (before editorial process) version of the published chapter 10.1016/B978-0-12-801238-3.11683-6</description>
  </descriptions>
</resource>
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