@phdthesis{Kacprowski2018,
  author    = {Tim Kacprowski},
  title     = {Omics Profiling and Biomarker Mining for Common Diseases},
  journal    = {Analyse von Omik-Daten und Biomarkersuche im Kontext h{\"a}ufiger Krankheiten},
  url       = {https://nbn-resolving.org/urn:nbn:de:gbv:9-002974-0},
  year      = {2018},
  abstract  = {Humanity is plagued by many diseases. Beside environmental influences, many --- if not all --- diseases are also subject to genetic predisposition and then display molecular alterations such as proteomic or metabolic aberrations. The elucidation of the molecular principles underlying human diseases is one of the prime goals of biomedical research. To this end, there has been an advent of large-scale omics profiling studies. While the field of molecular biology has experienced tremendous development, data analysis remains a bottleneck. In the context of this thesis, we developed a number of analysis strategies for different types of omics data resulting from different experimental settings. These include approaches for associations studies for plasma miRNAs and time-resolved plasma omics data. Furthermore, we devised analyses of different RNA-Seq transcriptome profiling studies coping with problems such as lack of replicates or multifactorial experimental design. We also designed machine learning frameworks for the identification of discriminatory biomolecular signatures analysing case-control or time-to-event data. All of the strategies mentioned above were developed and applied in the contexts of multi-disciplinary endeavours. They aided in the identification of plasma miRNAs associated with age, sex, and BMI as well as plasma miRNAs bearing potential as diagnostic biomarkers for non-alcoholic fatty liver disease (NAFLD). This thesis significantly contributed to a study demonstrating the utility of plasma miRNAs as prognostic biomarkers for major cardiovascular events such as ST-elevation myocardial infarction. Our approaches for analysing RNA-Seq data aided in the characterisation of murine models for Alzheimers disease and the transcriptional response of human gingiva fibroblasts to ionizing radiation exposure. Furthermore, the developed approaches were applied for studying a human model for thyrotoxicosis and for the successful identification of a multi-omics plasma biomarker signature of thyroid status. We are only beginning to understand the molecular principles underlying human diseases. The approaches and results presented in this thesis will contribute to improved understanding of biomolecular processes involved in common diseases such as Alzheimers disease, NAFLD, and cardiovascular diseases.},
  language  = {en}
}