Machine Learning, Big Data & AI

Invited Speakers

  • Dr James Cole, Kings College London
  • Dr Marwin Segler, BenevolentAI
  • Prof Tony Hey, Science and Technology Facilities Council (STFC)

Symposium Description

Artificial Intelligence (AI) is poised to have major impacts on many facets of biomedical research in the near future. The recent successes of deep learning in computer vision, natural language processing, game playing and autonomous vehicle development provide hints of what is to come from advances in AI applied to problems in biology and health research. Already major advances have occured through the use of AI methods in drug development, cancer diagnosis, pathology, genome sequence analysis, patient risk analysis and prediction of antibiotic resistance. Today it is becoming routine to build machine learning based predictive models where we have significant amounts of data but lack an underlying theory of mechanism. With careful uncertainty analysis these models can be used in place of traditional mechanistic models in some applications. Generative models can be used to create large collections of synthetic data that models real data, and can be used to generate drug candidates, example DNA sequences and to generate “synthetic patients” for computational analysis. In the future hybrid models that combine traditional first principles mechanistic models with those learned from data will out perform either individual model type.

This session will include speakers developing and using AI accross a range of problems from DNA sequence analysis, to drug design, prediction of phenotypes from genotypes and diagonosis diseases such as cancer, retinal myopathy, lung disease and the interpretion of medical records and to development of treatment strategies.

Symposium Chair

  • Rick Stevens, Associate Laboratory Director for Computing, Environment and Life Sciences