Development of machine learning mechanisms for better clinical trial design for rare disease.

We have entered into a new era of genetic therapies, with the first approved gene therapy, Luxturna, for biallelic RPE65 retinal dystrophy and a number of active clinical trials for both gene-replacement and small molecule drugs targeting particular genetic mutations. One of the challenges to bringing these new therapies to trial is the high costs associated with long trials due to challenges at present is predicting degeneration rates based on previous history. The second challenge is needing large numbers of patients in rare disease case, whether large number of patients may not exist.
This project will develop and apply time series machine learning (ML) tools to the existing cohorts of data labels. These models will look to combine the multiple factors into a single model to predict progression of each eye. Once this model is developed it can be applied other disease. The ideal result would develop a high specificity profile which can be used in designing cohorts for clinical trials. By better predicting natural disease course, trial cohorts can more accurately be developed and trial efficacy is determined. Through the combination of machine learning, computer vision and medicine, this project hopes to investigate the following questions.

Ideal person specification
• A good degree (2.1 or above; or equivalent EU/overseas degree) in computer science or have a strong computational background
• Have previous experience in image processing, especially in context of medical images
• Have significant understanding of Machine Learning techniques, specifically in relation to medical image classification
• Good analytical/mathematical skills, preferably with some knowledge of statistical approaches
• Good communication skills - especially in written English
• Very strong work ethic, with the ability to think creatively and work within a team

Duties and Responsibilities
• Development of some image analysis routines to facilitate data labelling
• Development of Machine Learning models, specifically General Adversarial Networks, Spare Data reconstruction
• Work in collaboration with other researchers especially medical professionals
• Prepare presentations, including text and images, for delivery by self and others.
• Travel for collaboration and other meetings or conferences.
• Prepare manuscripts for submission to peer-reviewed journals.
• Contribute to the overall activities of the research team, department and be aware of UCL policies.


Please contact Dr Adam Dubis ([Email Address Removed]) or Dr Waty Lilaonitkul ([Email Address Removed]) for more information

Application is by CV and covering letter emailed to Hattie de Laine ([Email Address Removed])