Dr Chris Bryant

Post Nominals	PhD MSc BSc (Hons)
Biography	I was the Principal Investigator on the EPSRC project "Efficient Biological Grammar Acquisition" (GR/S68682, £110K). I worked on the EPSRC project "Closed Loop Machine Learning" (GR/M56067) which culminated in The Robot Scientist (see Nature 427(6971):247-252, 2004).
Research Interests	The development and application of machine learning algorithms. Areas of machine learning of interest include rule induction, relational data mining and inductive logic programming. The main focus of the applications are contemporary, challenging problems in molecular biology. Specific interests include using machine learning for: Forming hypotheses, devising trials to discriminate between these competing hypotheses, and then using the results of these trials to converge upon an accurate hypothesis. Automatically generating grammars for biological sequences. Discovering refinements to biological networks, such as metabolic pathways. Previous real-world applications include: Predicting which of the upstream Open Reading Frames in S.cerevisiae regulate gene expression. Discovering how genes participate in the aromatic amino acid pathway of S.cerevisiae. Predicting the coupling preference of GPCR proteins. Recognising human neuropeptide precursors. Recommending chiral stationary phases based on the structural features of an enantiomer pair.
Teaching and Learning	Leader of the modules Database Systems (CRN 32741, UMC G400 10045) and Artificial Intelligence and Data Mining (CRN 34123, UMC G400 20077). Co-leader of the module Dependable Software Engineering (CRN 50257, UMC I100 30006).