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Dr Chris H. Bryant
School of ComScience&Eng
University of Salford

Inductive logic programming

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:

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.


Most Viewed Items

Item titleViews
1Combining active learning with inductive logic programming to close the loop in machine learning1253
2Data mining via ILP: The application of progol to a1128
3Using inductive logic programming to discover knowledge hidden in chemical data1057
4Knowledge discovery in databases: application to chromatography1008
5Combining inductive logic programming, active learning and robotics to discover the function of genes1007
6A first step towards learning which uORFs regulate gene expression999
7Towards an expert system for enantioseparations: induction of rules using machine learning997
8Discovering knowledge hidden in a chemical database using a commercially available data mining tool985
9A review of expert systems for chromatography975
10The validation of formal specifications of requirements961