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New computational methods for protein function prediction using structural, binding and sequence data

Funder: UK Research and InnovationProject code: G0902106
Funded under: MRC Funder Contribution: 400,905 GBP

New computational methods for protein function prediction using structural, binding and sequence data

Description

One aspect of my work is to develop statistical methods to analyse data from the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Data from this trial consists of up to six annual sets of questionnaires from each participant. The questionnaires assess anxiety, sexual function and acceptability of the screening process. I will model how these three processes change over time, and how they depend on one another. There are several approaches to analysing this type of data, and I will explore the pros and cons of three different methods. The challenge in this work lies in the complexity of the UKCTOCS data-set. I will also use some of these methods to analyse data from the MRC Cognitive Function and Ageing Study. A second aspect of my work is to develop methods for use in meta-analysis. Meta-analysis refers to the pooling of data from different studies, and is used to provide an overview of the available evidence. I will investigate methods for the meta-analysis of data which consists of times to events. I will also explore the meta-analysis of treatment `networks‘, investigating the relative effects of a set of treatments for a given condition.

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