TY  - GEN
Y1  - 2024///
CY  - Heidelberg
A1  - Capraz, Klemens Tümay
ID  - heidok35515
N2  - Disease progression and response to treatments can strongly differ between
patients, due to each individual?s unique genetic, environmental and molecular factors. Precision medicine aims to understand these factors in disease context and tailor treatments to patients based on their molecular pro-
file. High-throughput multi-omics techniques are powerful tools to measure
molecular profiles of cells, tissues and organs. They offer exciting possibilities in precision medicine and biomarker discovery, because they allow for
comprehensive and large-scale measurements of genes, proteins, metabolites, microbiome composition and other biological information. The gained
data?s complexity and high-dimensionality provide an opportunity to capture a lot of useful information about a biological sample. However, the
vastness of the data also makes interpretation challenging. Moreover, we
often do not have access to a large number of samples in biological settings,
which poses additional challenges to data analysis. To be able to understand
and identify patterns in complex multi-omics data, we need dimensionality
reduction methods. In this thesis, I start with presenting a short overview
of the omics modalities we can measure, I explain how they can be used in
precision medicine, and I present the most common dimensionality reduction methods used in biological data analysis. In the subsequent chapters I
introduce novel methods I developed and used for high-throughput biological data analysis. I present a new feature selection method based on replicate reproducibility, a Factor Analysis model that allows the incorporation of
prior knowledge, and a computational method to generate mutant libraries
for high-throughput screening for reverse genetics studies of gut microbes.
All methods I introduce are implemented in Python or R packages and are
available as open-source software.
AV  - public
TI  - Dimensionality reduction methods for high-dimensional biological data analysis
UR  - https://archiv.ub.uni-heidelberg.de/volltextserver/35515/
ER  -