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CH Bryant

Contact
Biography
My Most Viewed
My Latest

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Contact

Name:

CH Bryant

Email:

C.H.Bryant@salford.ac.uk

Department:

School of Science, Engineering & Environment

Organisation:

University of Salford

Address:

Room 213, Newton Building, School of Computing, Science & Engineering, University of Salford, Salford, Greater Manchester M5 4WT.

Country:

Homepage:

http://www.salford.ac.uk/computing-science-engineering/cse-academics/chris-bryant

profile_image:

Biography

Expertise:

Inductive logic programming

Job title:

Lecturer

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.

My Most Viewed

#	Item title
1.	The validation of formal specifications of requirements	1117	461
2.	Combining inductive logic programming, active learning and robotics to discover the function of genes	936	787
3.	Combining active learning with inductive logic programming to close the loop in machine learning	769
4.	Data mining via ILP: The application of progol to a	537	642
5.	Developing a logical model of yeast metabolism	513	259
6.	A review of expert systems for chromatography	501	2869
7.	Predicting functional upstream open reading frames in Saccharomyces cerevisiae	495
8.	Discovering knowledge hidden in a chemical database using a commercially available data mining tool	491	350
9.	Knowledge discovery in databases: application to chromatography	487	548
10.	Functional genomic hypothesis generation and experimentation by a robot scientist	485

My Latest

Othman, O and Bryant, CH ORCID: https://orcid.org/0000-0002-9002-8343 2015, 'Pruning classification rules with instance reduction methods' , International Journal of Machine Learning and Computing, 5 (3) , pp. 187-191.
Othman, Osama and Bryant, CH ORCID: https://orcid.org/0000-0002-9002-8343 2013, 'Preceding rule induction with instance reduction methods' , in: Proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern Recognition. , Lecture Notes in Computer Science (7988) , Springer, Berlin, pp. 209-218.
Kalantari, R and Bryant, CH ORCID: https://orcid.org/0000-0002-9002-8343 2012, 'Comparing the performance of object and object relational database systems on objects of varying complexity' , in: Proceedings of the 27th British National Conference on Databases , Lecture Notes in Computer Science (6121) , Springer, Berlin.
Selpi, Selpi, Bryant, CH ORCID: https://orcid.org/0000-0002-9002-8343, Kemp, GJL, Sarv, J, Kristiansson, E and Sunnerhagen, P 2009, 'Predicting functional upstream open reading frames in Saccharomyces cerevisiae' , BMC Bioinformatics, 10 , p. 451.
Selpi, S, Bryant, CH ORCID: https://orcid.org/0000-0002-9002-8343 and Kemp, GJL 2008, 'Using mRNA secondary structure predictions improves recognition of known yeast functional uORFs' , in: Proceedings of 2nd International Workshop on Machine Learning in Systems Biology , University of Liege, pp. 85-94.
Ray, O and Bryant, CH ORCID: https://orcid.org/0000-0002-9002-8343 2008, 'Inferring the function of genes from synthetic lethal mutations' , in: Complex, Intelligent and Software Intensive Systems , IEEE Computer Society, USA, pp. 667-671.
Mamer, T, Bryant, CH ORCID: https://orcid.org/0000-0002-9002-8343 and McCall, JM 2008, 'L-modified ILP evaluation functions for positive-only biological grammar learning' , in: Inductive logic programming , Lecture notes in artificial intelligence (5194) , Springer, Berlin / Heidelberg, Germany, pp. 176-191.
Fredouille, DC, Bryant, CH ORCID: https://orcid.org/0000-0002-9002-8343, Jayawickreme, CK, Jupe, S and Topp, S 2007, 'An ILP refinement operator for biological grammar learning' , in: Inductive logic programming , Lecture notes in artificial intelligence (subseries of Lecture notes in computer science) (4455) , Springer, Berlin / Heidelberg, Germany, pp. 214-228.
Bryant, CH ORCID: https://orcid.org/0000-0002-9002-8343, Fredouille, DC, Wilson, A, Jayawickreme, CK, Jupe, S and Topp, S 2006, 'Pertinent background knowledge for learning protein grammars' , in: Machine learning: ECML 2006 , Lecture notes in artificial intelligence (subseries of Lecture notes in computer science) (4212) , Springer, Berlin / Heidelberg, Germany, pp. 54-65.
Bryant, CH ORCID: https://orcid.org/0000-0002-9002-8343, Kemp, GJL and Cvijovic, M 2006, 'A first step towards learning which uORFs regulate gene expression' , Journal of Integrative Bioinformatics, 3 (2) , p. 31.