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  <abstract type="Summary">The ever-increasing amount and diversity of biological and medical data is a major challenge in computational analyses. Computational methods have to be combined into analysis workflows for seamless, swift, and transparent computation. In this work, numerous workflows were developed for the general processing of bulk RNA sequencing (RNA-Seq), single-cell sequencing experiments, and non-coding RNA identification. Mathematical concepts of machine learning and univariate meta-analyses were successfully implemented to independently investigate the role of cell therapies in cardiac regeneration.&lt;eng&gt;</abstract>
  <abstract type="Summary">Die  wachsende Menge und Vielfalt biologischer und medizinischer Daten ist eine große Herausforderung in Computeranalysen. Berechnungsmethoden müssen für eine schnelle und transparente Berechnung zu Workflows kombiniert werden. In dieser Arbeit wurden Workflows für die allgemeine Verarbeitung von RNA-Sequenzierung (RNA-Seq) sowie Einzelzell RNA-Seq und die nichtkodierende RNA-Identifizierung entwickelt. Mathematische Konzepte des maschinellen Lernens und univariate Metaanalysen wurden erfolgreich implementiert, um die Rolle von Zelltherapien bei der Herzregeneration zu untersuchen.&lt;ger&gt;</abstract>
  <note type="statement of responsibility">vorgelegt von Markus Wolfien</note>
  <note>GutachterInnen: Olaf Wolkenhauer (Universität Rostock) ; David Ellinghaus (Christian-Albrechts-Universität zu Kiel) ; Stefan Simm (Universitätsmedizin Greifswald)</note>
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