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Machine Learnable Fold Space Representation based on Residue Cluster Classes

Corral-Corral, Ricardo; Del Rio, Gabriel; Chavez, Edgar


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  <identifier identifierType="URL">https://zenodo.org/record/50192</identifier>
  <creators>
    <creator>
      <creatorName>Corral-Corral, Ricardo</creatorName>
      <givenName>Ricardo</givenName>
      <familyName>Corral-Corral</familyName>
      <affiliation>Department of Biochemistry and Structural Biology, Instituto de Fisiologa Celular, Universidad Nacional Autónoma de México, México D. F., México</affiliation>
    </creator>
    <creator>
      <creatorName>Del Rio, Gabriel</creatorName>
      <givenName>Gabriel</givenName>
      <familyName>Del Rio</familyName>
      <affiliation>Department of Biochemistry and Structural Biology, Instituto de Fisiologa Celular, Universidad Nacional Autónoma de México, México D. F., México</affiliation>
    </creator>
    <creator>
      <creatorName>Chavez, Edgar</creatorName>
      <givenName>Edgar</givenName>
      <familyName>Chavez</familyName>
      <affiliation>Centro de Investigación Científica y de Educación Superior de Ensenada, México</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Machine Learnable Fold Space Representation based on Residue Cluster Classes</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2015</publicationYear>
  <subjects>
    <subject>Computational Biology</subject>
    <subject>Machine Learning</subject>
    <subject>Protein Structure</subject>
    <subject>CATH</subject>
    <subject>SCOP</subject>
    <subject>Protein Fold Space</subject>
    <subject>Sperner Family</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2015-12-01</date>
  </dates>
  <resourceType resourceTypeGeneral="JournalArticle"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/50192</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsSupplementedBy">10.5281/zendoo.50193</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1016/j.compbiolchem.2015.07.010</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;&lt;strong&gt;Abstract&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Motivation&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Protein fold space is a conceptual framework where all possible protein folds exist and ideas about protein structure, function and evolution may be analyzed. Classification of protein folds in this space is commonly achieved by using similarity indexes and/or machine learning approaches, each with different limitations.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Results&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;We propose a method for constructing a compact vector space model of protein fold space by representing each protein structure by its residues local contacts. We developed an efficient method to statistically test for the separability of points in a space and showed that our protein fold space representation is learnable by any machine-learning algorithm.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Availability&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;An API is freely available at&amp;nbsp;https://code.google.com/p/pyrcc/.&lt;/p&gt;</description>
  </descriptions>
</resource>
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