Probabilistic components of molecular interactions and drug responses

A fundamental question in medicine is how cancer and other complex diseases operate on the molecular level. Identifying the detailed mechanisms and interactions of how diseases progress and respond to drug treatments is essential for developing effective therapies. High-throughput molecular profiling technologies have provided vast amounts of measurement data of these phenomena. However, making sense of these masses of data is far from straightforward and requires advanced computational analysis methods.

Probabilistic component models have been proven an effective tool in analysing and integrating high-dimensional and noisy molecular profiling data sources, such as gene expression. Such models can identify coherent components from the data, and interpreting these components provides insights about the underlying biological processes, such as disease progression and drug responses. In this thesis, probabilistic component models are applied and extended to identify and analyse molecular interaction and drug response patterns.