This thesis describes the design, implementation and application of bioinformatics systems to aid work in the field of biomarker discovery and diagnostic test development. The aim of the work was to develop a flexible data storage and analysis platform that would be capable of housing and working with data from a variety of modern biomarker analysis techniques. In order to achieve this aim, several tools were developed: a flexible database schema, taking ideas from the field of systems biology, was developed with the goal of being flexible enough to house information about experiments looking at targets such as genes, proteins and metabolites; and API was created to allow easy programmatic interaction with the database; and multivariate data analysis routines were prepared so that data imported into the database could be investigated. Together this toolset was named XPA [for ‘Cross Platform Analysis’]. The XPA system was tested by using it to house and analyse data from two different medical studies, one using quantitative PCR [qPCR] to observe gene expression changes in prostate cancer, and the second using surface enhanced laser desorption/ionisation mass spectrometry [SELDI MS] to generate protein profiles in sufferers of pre-eclampsia. In both studies XPA was used to develop multivariate classification models using partial least squares discriminant analysis [PLS-DA] and support vector machines [SVMs], with the aim of evaluating the data acquired for potential diagnostic use. The results showed the benefit of a tool such as XPA to the field of biomarker discovery
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