Expertise:
Inductive logic programming

Biography:
Chris Bryant is interested in tackling contemporary, challenging problems in molecular biology using a branch of machine learning known as inductive logic programming. He has worked in the following areas previously. Predicting the coupling preference of GPCR proteins. Recognising human neuropeptide precursors. 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. Recommending chiral stationary phases based on the structural features of an enantiomer pair. For more information, see: http://www.cse.salford.ac.uk/profiles/bryant/interests.php

Qualifications:
PhD, University of Manchester Institute of Science and Technology (UMIST). MSc Applied Artificial Intelligence, University of Aberdeen. BSc (Hons) Combined Studies in Science: Chemistry and Computing, Sunderland University.