Novel methodology for predicting the functional impact of mutational variation in the human genome.

Lead Research Organisation: University of Bristol
Department Name: Engineering Mathematics

Abstract

Rapid developments in next-generation sequencing technologies has lead to a substantial
increase in identified genetic mutations, many of which may be implicated in disease. The
focus of the PhD programme is to construct algorithmically-based methods for predicting the
functional consequence of genetic variants, a problem where we have already achieved
some state-of-the-art results in certain contexts. Particular themes will be novel
methodology associated with data integration, rare variant combinations and probabilistic
inference. We expect to use the methods developed with data from the
Genomics England (100,000 genomes) project.

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509619/1 01/10/2016 30/09/2021
1838073 Studentship EP/N509619/1 21/11/2016 20/05/2020 James Fearn
 
Title Phylogenetic Relevance Vector Machine (phyRVM) 
Description An extension to the relevance vector machine which takes into account the genetic non-independence of living organisms by incorporating the phylogenetic covariance matrix into the likelihood. If a diagonal covariance matrix is used, the model can learn optimal branch lengths for each taxa to the root of the tree. The model performs feature selection by learning a relevance weight for each feature. 
Type Of Material Computer model/algorithm 
Year Produced 2019 
Provided To Others? No  
Impact None