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Funktionelles Clustering Von Genen Mit Der Gene Ontology

Funktionelles Clustering Von Genen Mit Der Gene Ontology

          
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About the Book

Mit der Erfindung und dem Einsatz von Hochdurchsatzmethoden in der Molekularbiologie, sind Forscher in der Lage, sehr grosse Datenmengen in relativ kurzer Zeit zu sammeln. Wahrend der Analyse dieser Daten wird schnell deutlich, dass die Integration von bereits vorhandenem biologischen Wissen in den Analyseprozess eine der neuen grossen Herausforderungen der Bioinformatik darstellt. Die vorliegende Arbeit leistet hierzu einen Beitrag und konzentriert sich auf das Clustering von Genen bzgl. ihrer Funktion. Sie beschaftigt sich in diesem Zusammenhang mit den drei Teilbereichen Clustervalidierung, Clusteralgorithmen und biologische Ahnlichkeitsfunktionen. Bekannte Clusterindices werden erstmals mit biologischen Distanzmassen kombiniert und sofern notig dafur erweitert. Die entwickelten Indices erlauben eine biologische Evaluation von Gengruppen, wie z.B. Genexpressionsclustern oder Clustern von Gensequenzen, um die Cluster herauszufinden, die biologisch interessant sind. Die dazu benotigten biologischen Distanzmasse fur Gene werden mithilfe von Gene Ontology Annotationen berechnet, die in den meisten offentlichen Datenbanken verfugbar sind. Die Gene Ontology (GO) ist eine Taxonomie, die fachspezifisches, biologisches Wissen in einer fur Computer prozessierbaren Form zur Verfugung stellt. Das Clustering mit GO-basierten Distanzen stellt an den verwendeten Clusteralgorithmus andere Anspruche als ein rein numerischer Datensatz, da z.B. GO-basierte Mittelwerte zwischen Genen nicht berechnet werden konnen. Viele konventionelle Clusteralgorithmen werden vor diesem Hintergrund unbrauchbar. In der vorliegenden Dissertation wurde daher ein neues, auf einem Evolutionaren Algorithmus basierendes, Clusterverfahren (MST-MA) entwickelt. Der Einfluss der verschiedenen Parameter des MST-MA auf die Qualitat der gefundenen Losungen wird untersucht, und der MST-MA wird mit anderen fur das Problem geeigneten Clusterverfahren verglichen. Des Weiteren sind bei der Arbeit mit den vorhandenen biologischen Distanzfunktionen fur Gene einige gravierende Mangel dieser Unahnlichkeitsmasse deutlich geworden. Daher wurden vier neue Ahnlichkeitsfunktionen entwickelt, bei denen diese Nachteile nicht mehr bestehen. Wir untersuchen und vergleichen die vier verschiedenen Ahnlichkeitsmasse ausfuhrlich und testen sie in Verbindung mit Spektralclustering, dualem K-Means und Average Linkage Clustering. Die gewahlte Reprasentation als Empirical Kernel Map ermoglicht, dass nun auch GO-basierte Mittelwerte zwischen den Genen berechnet werden konnen. Bei allen Ahnlichkeitsmassen handelt es sich um Kernelfunktionen, was sie auch fur andere Verfahren, wie z.B. Support Vektor Maschinen, interessant macht. Grundsatzlich sind die vorgestellten Ahnlichkeitsfunktionen jedoch offen fur jede Art von maschinellem Lernenverfahren, das funktionale Ahnlichkeiten verwenden soll.


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Product Details
  • ISBN-13: 9783832514068
  • Publisher: Logos Verlag Berlin
  • Binding: Paperback
  • Language: German
  • Returnable: N
  • Spine Width: 0 mm
  • Width: 145 mm
  • ISBN-10: 3832514066
  • Publisher Date: 24 Nov 2006
  • Height: 210 mm
  • No of Pages: 166
  • Series Title: German
  • Weight: 700 gr


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