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  <doc>
    <id>379</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>1998-12-18</completedDate>
    <publishedDate>1998-12-18</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">From Simulation Data to Conformational Ensembles: Structure and Dynamics based Methods</title>
    <abstract language="eng">Statistical methods for analyzing large data sets of molecular configurations within the chemical concept of molecular conformations are described. The strategies are based on dependencies between configurations of a molecular ensemble; the article concentrates on dependencies induces by a) correlations between the molecular degrees of freedom, b) geometrical similarities of configurations, and c) dynamical relations between subsets of configurations. The statistical technique realizing aspect a) is based on an approach suggested by {\sc Amadei et al.} (Proteins, 17 (1993)). It allows to identify essential degrees of freedom of a molecular system and is extended in order to determine single configurations as representatives for the crucial features related to these essential degrees of freedom. Aspects b) and c) are based on statistical cluster methods. They lead to a decomposition of the available simulation data into {\em conformational ensembles} or {\em subsets} with the property that all configurations in one of these subsets share a common chemical property. In contrast to the restriction to single representative conformations, conformational ensembles include information about, e.g., structural flexibility or dynamical connectivity. The conceptual similarities and differences of the three approaches are discussed in detail and are illustrated by application to simulation data originating from a hybrid Monte Carlo sampling of a triribonucleotide.</abstract>
    <identifier type="serial">SC-98-36</identifier>
    <identifier type="opus3-id">380</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-3797</identifier>
    <enrichment key="SourceTitle">Appeared in: J. Comp. Chemistry 20 (1999) pp. 1760-1774</enrichment>
    <author>Wilhelm Huisinga</author>
    <author>Christoph Best</author>
    <author>Frank Cordes</author>
    <author>Rainer Roitzsch</author>
    <author>Christof Schütte</author>
    <series>
      <title>ZIB-Report</title>
      <number>SC-98-36</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>conformational ensemble</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cluster method</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>structural and dynamical similarity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>representative</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>conformation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>essential degrees of freedom</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>transi</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="msc" number="62H30">Classification and discrimination; cluster analysis [See also 68T10]</collection>
    <collection role="institutes" number="">ZIB Allgemein</collection>
    <collection role="persons" number="schuette">Schütte, Christof</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/379/SC-98-36.ps</file>
    <file>https://opus4.kobv.de/opus4-zib/files/379/SC-98-36.pdf</file>
  </doc>
  <doc>
    <id>427</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>1999-11-26</completedDate>
    <publishedDate>1999-11-26</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Self-Organizing Maps Combined with Eigenmode Analysis for Automated Cluster Identification</title>
    <abstract language="eng">One of the important tasks in Data Mining is automated cluster analysis. Self-Organizing Maps (SOMs) introduced by {\sc Kohonen} are, in principle, a powerful tool for this task. Up to now, however, its cluster identification part is still open to personal bias. The present paper suggests a new approach towards automated cluster identification based on a combination of SOMs with an eigenmode analysis that has recently been developed by {\sc Deuflhard et al.} in the context of molecular conformational dynamics. Details of the algorithm are worked out. Numerical examples from Data Mining and Molecular Dynamics are included.</abstract>
    <identifier type="serial">SC-99-38</identifier>
    <identifier type="opus3-id">427</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-4279</identifier>
    <enrichment key="SourceTitle">Appeared in: Proc. of the 2nd Intern. ICSC Symposium on Neural Computation, ISCS Academic Press 2000</enrichment>
    <author>Tobias Galliat</author>
    <author>Wilhelm Huisinga</author>
    <author>Peter Deuflhard</author>
    <series>
      <title>ZIB-Report</title>
      <number>SC-99-38</number>
    </series>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Self-Organizing Maps</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cluster analysis</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="msc" number="15A18">Eigenvalues, singular values, and eigenvectors</collection>
    <collection role="msc" number="62H30">Classification and discrimination; cluster analysis [See also 68T10]</collection>
    <collection role="msc" number="68T05">Learning and adaptive systems [See also 68Q32, 91E40]</collection>
    <collection role="institutes" number="">ZIB Allgemein</collection>
    <collection role="persons" number="deuflhard">Deuflhard, Peter</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/427/SC-99-38.ps</file>
    <file>https://opus4.kobv.de/opus4-zib/files/427/SC-99-38.pdf</file>
  </doc>
</export-example>
