Exascale Compound Activity Prediction Engines

This is part of the €80 billion H2020 European Initiative with funding available over 7 years (2014 to 2020).

We produced state-of-the-art scalable machine learning algorithms with guaranteed performance for future Exascale machines to predict compound bioactivity.

Proteomic Analysis

This project was funded by St. Bartholomew Hospital, Western General Hospital Edinburgh, and the Veterinary Laboratories Agency.

We determine the potential for diagnosing various cancers and other diseases through the identification of proteomic biomarkers found in human serum.

Ovarian Cancer - Adnexal mass categorising

We worked with St. Bartholomew's in London to perform a diagnostic of ovarian cancer, using a variety of symptoms in 287 patients to distinguish benign adnexal masses frommalignant.

The above images a patient's scan over a period of 24 hours, with the variation being analysed.

Mining the Network Behaviour of Bots

This project makes cluster-based analysis more accurate and robust against arbitrary obfuscation-based evasion attacks. A powerful clustering method is based on nonparametric probability density estimation.

In addition, the property of validity can be used to control the number of "alarms" (predicting that a host is compromised) raised by a bot detection algorithm. This is valuable in situations where alarms have to be investigated by human experts but the available manpower is limited.

Abdominal pain diagnostic tool

In partnership with Western General Hospital, Edinburgh, we developed a Bayesian algorithm for the diagnosis of abdominal pain in patients.

We analysed data on 6,387 patients, each suffering abdominal pains described by one or more of 33 identified symptoms. Given this data, the learning machine (a G&T system) outputs a probability that the patient was suffering from each of 9 separate diseases.

LINK Hydrocarbon Reservoir

We worked with Shell International, Cambridge Carbonates Ltd, British Gas Ltd and Petro-Canada to create a permeability predictor database for use in oil exploration.

The CLRC contributes a learning machine to create a method of comparison, based on analysis of porosity and permeability data, for determining the porosity and permeability of unknown samples.

Veterinary Laboratories Agency project

This is a collaborative project with the Veterinary Laboratories Agency (VLA) entitled "Development and Application of Machine Learning Algorithms for the Analysis of Complex Veterinary Data Sets", started in 2008.

Offender profiling

Using a database set up in 1986 by Derbyshire Constabulary and the input of detectives, we developed an offender profiling algorithm that allowed detectives to input a known variable (eg. Method of killing) to extrapolate further details (probabilities thereof) about the offender. Such a tool can help focus the limited resources of the investigating detectives. With recent incidents driving a call for improved police databases, such tools could become more prevalent in modern police work.