Omics Analytics
We’re developing new machine-learning methods in omics analytics to discover the drivers of disease – at both the population and individual level.
The rapid evolution of AI-driven multi-omics research is transforming biomedical science. Omics data sets are huge, complex and differ widely. A major challenge in this field is in taking the big data being gathered in these genetic, transcriptomic, proteomic and metabolomic data sets and integrating it, often in combination with patients’ clinical data, to extract actionable new intelligence.
Advanced machine learning techniques – particularly deep learning methods – are essential here to build our knowledge of disease, improve individualised diagnosis/prognosis and develop targeted, personalised treatments.
CCAIM is developing new machine-learning methods in omics analytics to discover the drivers of disease – at both the population and individual level. Our techniques are also 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.
Omics analytics is a burgeoning field, with countless opportunities for exploration, so it is important that the CCAIM team offers world-class expertise in this area. In particular, Dr Sarah Teichmann, who directs the Cellular Genetics Programme at the Sanger Institute and develops computational methods to explore genomics and biology. Dr Teichmann’s discoveries have transformed our understanding of fundamental biological pathways in health and disease.
