Inferring Complex Hidden Causes of Disease through Probabilistic Models



The increased ability to measure individuals’ genetic profiles ( through single nucleotide polymorphisms), combined with the ability to characterize a disease activity through gene expression, and other biomarkers, should reveal more realistically complex webs of causal factors for disease. Understanding these causal factors would enable personalised interventions targeted to an individual’s genetic, environmental (including concurrent treatments) and treatment preference profile.

This vision is challenging on two fronts: first, integration of different sources of (genomic) biological data is not straightforward; second, environments are not artificially controlled. In practice, disease occurrence and progression is often triggered by a combination of genetic and environmental factors. Environmental factors need to be assimilated with the genetic background (through the genomic data) and placed in a unified modelling framework to characterize the disease.

The objective of this project is to address these issues. Our aim is to perform statistical inference from these models to enable us to resolve the determinants of a given disease and its responses to treatments. The research will involve amalgamation of several different research areas, covering health, biology, computational and statistical inference.

The project will involve a large amount of mathematics, in particular probability theory and advanced linear algebra.