|Room 213, Newton Building, School of Science, Engineering and Environment, University of Salford, United Kingdom, M5 4WT.|
Chris Bryant's research is concerned with 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:
- automating some aspects of the scientific method - Forming hypotheses, devising trials to discriminate between these competing hypotheses, and then using the results of these trials to converge upon an accurate hypothesis.
- biological grammar acquisition - generating grammars for biological sequences using machine learning;
- refinement of biological networks - Discovering refinements to biological networks, such as metabolic pathways, using machine learning.
Previous real-world applications include:
- 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.
PhD Thesis Title: Inference of Gene Relations from Microarray Experiments by Abductive Reasoning
Candidate: Irene V. Papatheodorou
The World Technology Network short-listed The Robot Scientist for a World Technology Award under the IT - Software (Individual) category.
One of only two speakers from the UK.
One of only three speakers from the UK; the other five were from overseas.
Eighteenth workshop in the Machine Intelligence founded by Donald Michie in 1965.
One of only four speakers from the UK; the other eleven were from overseas.
The only person from the UK that presented a paper.
One of only two reviewers during the 2nd year of the EU Framework VI project (FP6-508861) entitled "Application of Probabilistic Inductive Logic Programming II".