Universität zu Köln

Baar, Till ORCID: 0000-0002-6744-1463 (2022). Regression Models for High-Dimensional, Biological Data. PhD thesis, Universität zu Köln.

Preview

PDF dissertationtillbaar.pdf - Published Version Download (1MB) | Preview

Abstract

In this cumulative dissertation, statistical models for regression are discussed in light of high-dimensional, biological data. The dissertation includes three publications: RNA transcription and degradation of Alu retrotransposons depends on sequence features and evolutionary history examines Alu elements, RNA retrotransposons in the human genome. Their RNA metabolism is poorly understood, and the source of Alu transcripts is still unresolved. We have conducted a transcription shutoff experiment and metabolic RNA labelling to shed further light on the life cycle of Alu transcripts.We furthermore present a novel statistical test for detecting expression quantitative trait loci relying on k-mer sequence representation. Endoscopic hemostasis makes the difference: Angiographic treatment in patients with lower gastrointestinal bleeding uses retrospective study data from patients receiving either endoscopic or angiographic treatment for lower gastrointestinal bleeding. While a majority of patients can be treated successfully with the usually preferred endoscopic method, in some cases, angiography is required to achieve hemostasis. Using conditional inference trees, we construct a decision tree model predicting if a patient should receive angiographic treatment. Genetic instability and recurrent MYC amplification in ALK-translocated NSCLC: a central role of TP53 mutations investigates a molecular subtype of lung cancer exhibiting rearrangements of the ALK gene. This cancer type often resists treatments, and no reliable biomarker to identify patients at risk for relapse is known. Analysing biopsy and cell culture data, we find that mutations in the TP53 gene can lead to chromosomal instability and thus the amplification of known cancer genes. This, in turn, grants cancer cells a proliferative advantage compared to the wild-type, providing a new approach for diagnosis and treatment.

Item Type:	Thesis (PhD thesis)
Creators:	Creators Email ORCID ORCID Put Code Baar, Till till.baar@uni-koeln.de orcid.org/0000-0002-6744-1463 115337164
URN:	urn:nbn:de:hbz:38-621771
Date:	2022
Language:	English
Faculty:	Faculty of Mathematics and Natural Sciences Faculty of Medicine
Divisions:	Faculty of Medicine > Medizinische Statistik und Bioinformatik > Institut für Medizinische Statistik und Bioinformatik – IMSB
Subjects:	Data processing Computer science General statistics Life sciences Medical sciences Medicine
Uncontrolled Keywords:	Keywords Language Computational Biology English Alu Elements English Alu transcription English RNA labeling English retrotransposons English
Date of oral exam:	1 June 2022
Referee:	Name Academic Title Tresch, Achim Prof. Dr. Beyer, Andreas Prof. Dr.
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/62177