Graphical models for biclustering and information retrieval in gene expression data
Abstract
The cell coordinates its biological response to the environment partly via the selective synthesis of thousands of unique RNA and protein molecules. Understanding the molecular biology of the cell is thus essential to the advancement of areas such as health care, agriculture, and energy production, but requires the ability to simultaneously acquire information about thousands of molecules in a sample. Recent high-throughput measurement technologies address this concern. While being useful, they generate a high volume of data and bring in methodological challenges, effectively shifting the bottleneck in molecular biology research from data acquisition to data analysis. In particular, an important challenge is the genome-wide analysis of how RNA is transcribed under different conditions, organisms, and tissues, a process known as gene expression.\nWhen developing computational methods for biological data analysis tasks, probabilistic frameworks constitute promising approaches due to their flexibility, soundness, and ability to handle noisy data. In this thesis, the contributions are in the development of probabilistic methods for two relevant tasks in genome-wide gene expression analysis, namely biclustering and information retrieval.\nBiclustering concerns the simultaneous grouping of objects, e.g., genes, and conditions. The first contribution is the development of a Bayesian extension to an existing biclustering model. The second contribution is a novel probabilistic method…
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