<> "The repository administrator has not yet configured an RDF license."^^ . <> . . "Dimensionality reduction methods for high-dimensional biological data analysis"^^ . "Disease progression and response to treatments can strongly differ between\r\npatients, 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-\r\nfile. High-throughput multi-omics techniques are powerful tools to measure\r\nmolecular profiles of cells, tissues and organs. They offer exciting possibilities in precision medicine and biomarker discovery, because they allow for\r\ncomprehensive and large-scale measurements of genes, proteins, metabolites, microbiome composition and other biological information. The gained\r\ndata’s complexity and high-dimensionality provide an opportunity to capture a lot of useful information about a biological sample. However, the\r\nvastness of the data also makes interpretation challenging. Moreover, we\r\noften do not have access to a large number of samples in biological settings,\r\nwhich poses additional challenges to data analysis. To be able to understand\r\nand identify patterns in complex multi-omics data, we need dimensionality\r\nreduction methods. In this thesis, I start with presenting a short overview\r\nof the omics modalities we can measure, I explain how they can be used in\r\nprecision medicine, and I present the most common dimensionality reduction methods used in biological data analysis. In the subsequent chapters I\r\nintroduce 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\r\nprior knowledge, and a computational method to generate mutant libraries\r\nfor high-throughput screening for reverse genetics studies of gut microbes.\r\nAll methods I introduce are implemented in Python or R packages and are\r\navailable as open-source software."^^ . "2024" . . . . . . . "Klemens Tümay"^^ . "Capraz"^^ . "Klemens Tümay Capraz"^^ . . . . . . "Dimensionality reduction methods for high-dimensional biological data analysis (PDF)"^^ . . . "thesis_capraz.pdf"^^ . . . "Dimensionality reduction methods for high-dimensional biological data analysis (Other)"^^ . . . . . . "indexcodes.txt"^^ . . . "Dimensionality reduction methods for high-dimensional biological data analysis (Other)"^^ . . . . . . "lightbox.jpg"^^ . . . "Dimensionality reduction methods for high-dimensional biological data analysis (Other)"^^ . . . . . . "preview.jpg"^^ . . . "Dimensionality reduction methods for high-dimensional biological data analysis (Other)"^^ . . . . . . "medium.jpg"^^ . . . "Dimensionality reduction methods for high-dimensional biological data analysis (Other)"^^ . . . . . . "small.jpg"^^ . . "HTML Summary of #35515 \n\nDimensionality reduction methods for high-dimensional biological data analysis\n\n" . "text/html" . .