Research papers published in leading journals and accepted at renowned machine learning conferences.
Papers from our Faculty members that have been accepted/presented at world-renowned AI and ML conferences.
International Conference on Learning Representations (ICLR)
Neural Information Processing Systems (NeurIPS)
International Conference on Machine Learning (ICML)
The threat of coronavirus requires that we use every weapon in our scientific arsenal, from advances in fundamental science to capacity planning in intensive care departments and developing new approaches to clinical trials.
Our expert network brings pioneering AI and ML to bear on COVID-19.
Scientists are currently hampered in the discovery of optimal drug targets by a failure to understand biological processes at a systems level.
Our aim in the biomedical arena is to leverage structural, metabolic and genetic metadata to develop multiscale generative models – at the level of molecules, cells, organs and whole organisms – to uncover novel targets for antibiotics and host-directed therapies.
We are also developing new, interpretable deep learning methods to better predict the functional impacts of the protein mutations seen in antimicrobial resistance and cancer.
Next-Generation Clinical Trials
Randomised controlled trials (RCTs) are currently considered the gold standard but such clinical trials are often slow, costly and lack flexibility.
We’re investigating how to optimise patient recruitment for enrolment in clinical trials and how to conduct more efficient, responsive trials. ML-enhanced trials can speed up learning and significantly reduce error.
We are integrating omics data and a wide variety of other data sets, including electronic health records (EHRs), and applying novel machine learning techniques to better characterise individual patients, improve diagnosis, account for co-morbidities and reliably predict patient trajectories.
These technologies will enable the optimisation of targeted treatment interventions, identify looming health issues in large populations before they develop symptoms, and allow us to move towards the AI-enabled hospitals of tomorrow.
Our researchers are developing new machine-learning methods in omics analytics to discover the drivers of disease – at both the population and individual level.
Our techniques are allowing us to characterise clusters of cells or patients, enabling next-generation precision medicine that includes powerful predictive models of personalised disease trajectory, dynamic prognosis and likely responses to treatment.