Personalised medicine using Data Analytics and Artificial Inlligence

Lead Research Organisation: Brunel University London
Department Name: Computer Science

Abstract

The project will explore the interaction of genomic and clinical data using a variety of machine learning techniques. The focus will be on building predictive models that are personalized to specific cohorts of patients.

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509437/1 01/10/2016 30/09/2021
2008249 Studentship EP/N509437/1 01/01/2018 31/12/2021 Seyed Sajjadi
EP/R512990/1 01/10/2018 30/09/2023
2008249 Studentship EP/R512990/1 01/01/2018 31/12/2021 Seyed Sajjadi
 
Description I have discovered that analysing the topology (shape) of a dataset that consists of gene expression data shows promising results. Gene expression data can seem meaningless but looking at the topology and how clusters of the datasets (patients) link up could produce insightful information about the patients. Furthermore, pseudo-time series analysis of genomic data has been effective to see how the disease progresses through time of other attributes.

I have successful created a novel approach TDA-PTS, which I have published with the title "Building Trajectories Over Topology with TDA-PTS: An Application in Modelling Temporal Phenotypes of Disease" and also advanced this approach by implementing constraints to create CBPTS (Constraint-Based Pseudo-Time Series), which I will also be publishing my findings in the near future. Finally, I will attempt to find and gather properly clinician data to apply my 2 novel approaches
Exploitation Route The outcome of combining these two approaches produce a novel approach to dealing with genomic data and to see if this can work with other datasets.
Sectors Healthcare,Pharmaceuticals and Medical Biotechnology,Other