Christopher H. Bryant

According to our database, Christopher H. Bryant authored at least 16 papers between 1997 and 2013.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2013

Preceding Rule Induction with Instance Reduction Methods.

Osama Othman, Christopher H. Bryant

Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2013

2010

Comparing the Performance of Object and Object Relational Database Systems on Objects of Varying Complexity.

Reza Kalantari, Christopher H. Bryant

Proceedings of the Data Security and Security Data, 2010

2009

Predicting functional upstream open reading frames in <i>Saccharomyces cerevisiae</i>.

Selpi, Christopher H. Bryant, Graham J. L. Kemp, Janeli Sarv, Erik Kristiansson, Per Sunnerhagen

BMC Bioinform., 2009

2008

L-Modified ILP Evaluation Functions for Positive-Only Biological Grammar Learning.

Thierry Mamer, Christopher H. Bryant, John A. W. McCall

Proceedings of the Inductive Logic Programming, 18th International Conference, 2008

Inferring the Function of Genes from Synthetic Lethal Mutations.

Oliver Ray, Christopher H. Bryant

Proceedings of the Second International Conference on Complex, 2008

2006

A First Step towards Learning which uORFs Regulate Gene Expression.

Selpi, Christopher H. Bryant, Graham J. L. Kemp, Marija Cvijovic

J. Integr. Bioinform., 2006

An ILP Refinement Operator for Biological Grammar Learning.

Daniel Fredouille, Christopher H. Bryant, Channa K. Jayawickreme, Steven Jupe, Simon Topp

Proceedings of the Inductive Logic Programming, 16th International Conference, 2006

Pertinent Background Knowledge for Learning Protein Grammars.

Christopher H. Bryant, Daniel Fredouille, Alex Wilson, Channa K. Jayawickreme, Steven Jupe, Simon Topp

Proceedings of the Machine Learning: ECML 2006, 2006

2005

Speeding up Parsing of Biological Context-Free Grammars.

Daniel Fredouille, Christopher H. Bryant

Proceedings of the Combinatorial Pattern Matching, 16th Annual Symposium, 2005

2001

Are Grammatical Representations Useful for Learning from Biological Sequence Data? - A Case Study.

Stephen H. Muggleton, Christopher H. Bryant, Ashwin Srinivasan, Alex Whittaker, Simon Topp, Christopher J. Rawlings

J. Comput. Biol., 2001

Developing a Logical Model of Yeast Metabolism.

Philip G. K. Reiser, Ross D. King, Douglas B. Kell, Stephen H. Muggleton, Christopher H. Bryant, Stephen G. Oliver

Electron. Trans. Artif. Intell., 2001

Combining Inductive Logic Programming, Active Learning and Robotics to Discover the Function of Genes.

Christopher H. Bryant, Stephen H. Muggleton, Stephen G. Oliver, Douglas B. Kell, Philip G. K. Reiser, Ross D. King

Electron. Trans. Artif. Intell., 2001

2000

Theory Completion Using Inverse Entailment.

Stephen H. Muggleton, Christopher H. Bryant

Proceedings of the Inductive Logic Programming, 10th International Conference, 2000

Learning Chomsky-like Grammars for Biological Sequence Families.

Stephen H. Muggleton, Christopher H. Bryant, Ashwin Srinivasan

Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29, 2000

Measuring Performance when Positives Are Rare: Relative Advantage versus Predictive Accuracy - A Biological Case Study.

Stephen H. Muggleton, Christopher H. Bryant, Ashwin Srinivasan

Proceedings of the Machine Learning: ECML 2000, 11th European Conference on Machine Learning, Barcelona, Catalonia, Spain, May 31, 2000

1997

Data Mining via ILP: The Application of Progol to a Database of Enantioseparations.

Christopher H. Bryant

Proceedings of the Inductive Logic Programming, 7th International Workshop, 1997