Dr Chris Bryant
School of Science, Engineering & Environment
The Science Engineering and Environment Building
University of Salford,
University Road, Salford, M5 4QJ, United Kingdom.
Current positions
Lecturer
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).
Areas of Research
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.
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).
Qualifications
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PhD
1993 - 1996 -
MSc Applied Artificial Intelligence
1992 - 1993 -
BSc (Hons) Combined Studies in Science: Chemistry and Computing
1986 - 1990
Recognitions
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Elected member of the Senate of the University of Salford (2021 - 2024)
Publications
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Functional genomic hypothesis generation and experimentation by a robot scientist
King, R., Whelan, K., Jones, F., Reiser, P., Bryant, C., Muggleton, S., …Oliver, S. (2004). Functional genomic hypothesis generation and experimentation by a robot scientist. Nature, 427(6971), 247-252. https://doi.org/10.1038/nature02236
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Predicting functional upstream open reading frames in Saccharomyces cerevisiae
Selpi, S., Bryant, C., Kemp, G., Sarv, J., Kristiansson, E., & Sunnerhagen, P. (2009). Predicting functional upstream open reading frames in Saccharomyces cerevisiae. BMC Bioinformatics, 10, 451. https://doi.org/10.1186/1471-2105-10-451
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Combining inductive logic programming, active learning and robotics to discover the function of genes
Bryant, C., Muggleton, S., Kell, D., Reiser, P., King, R., & Oliver, S. (2001). Combining inductive logic programming, active learning and robotics to discover the function of genes
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An ILP refinement operator for biological grammar learning
Fredouille, D., Bryant, C., Jayawickreme, C., Jupe, S., & Topp, S. (2007). An ILP refinement operator for biological grammar learning. In S. Muggleton, R. Otero, & A. Tamaddoni-Nezhad (Eds.), Inductive logic programming (214-228). Berlin / Heidelberg, Germany: Springer. https://doi.org/10.1007/978-3-540-73847-3_24
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A first step towards learning which uORFs regulate gene expression
Bryant, C., Kemp, G., & Cvijovic, M. (2006). A first step towards learning which uORFs regulate gene expression. Journal of Integrative Bioinformatics, 3(2), 31. https://doi.org/10.2390/biecoll-jib-2006-31