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Dr Chris Bryant's Outputs (33)

Pruning classification rules with instance reduction methods (2015)
Journal Article
Othman, O., & Bryant, C. (2015). Pruning classification rules with instance reduction methods. International journal of machine learning and computing (Online), 5(3), 187-191. https://doi.org/10.7763/IJMLC.2015.V5.505

Generating classification rules from data often leads to large sets of rules that need to be pruned. A new pre-pruning technique for rule induction is presented which applies instance reduction before rule induction. Training three rule classifiers o... Read More about Pruning classification rules with instance reduction methods.

Comparing the performance of object and object relational database systems on objects of varying complexity (2012)
Presentation / Conference Contribution

This is the first published work to compare the performance of object and object relational database systems based on the object's complexity. The findings of this research show that the performance of object and object relational database systems ar... Read More about Comparing the performance of object and object relational database systems on objects of varying complexity.

Predicting functional upstream open reading frames in Saccharomyces cerevisiae (2009)
Journal Article
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

BACKGROUND: Some upstream open reading frames (uORFs) regulate gene expression (i.e., they are functional) and can play key roles in keeping organisms healthy. However, how uORFs are involved in gene regulation is not yet fully understood. In order t... Read More about Predicting functional upstream open reading frames in Saccharomyces cerevisiae.

Using mRNA secondary structure predictions improves recognition of known yeast functional uORFs (2008)
Book Chapter
Selpi, S., Bryant, C., & Kemp, G. (2008). Using mRNA secondary structure predictions improves recognition of known yeast functional uORFs. In L. Wehenkel, P. Geurts, Y. Moreau, & F. d'Alche-Buc (Eds.), Proceedings of 2nd International Workshop on Machine Learning in Systems Biology (85-94). University of Liege

We are interested in using inductive logic programming ILP)to generate rules for recognising functional upstream open reading frames (uORFs) in the yeast Saccharomyces cerevisiae. This paper empirically investigates whether providing an ILP system wi... Read More about Using mRNA secondary structure predictions improves recognition of known yeast functional uORFs.

A first step towards learning which uORFs regulate gene expression (2006)
Journal Article
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

We have taken a first step towards learning which upstream Open Reading Frames (uORFs) regulate gene expression (i.e., which uORFs are functional) in the yeast Saccharomyces cerevisiae. We do this by integrating data from several resources and combin... Read More about A first step towards learning which uORFs regulate gene expression.

Are grammatical representations useful for learning from biological sequence data?— a case study (2001)
Journal Article
Muggleton, S., Bryant, C., Srinivasan, A., Whittaker, A., Topp, S., & Rawlings, C. (2001). Are grammatical representations useful for learning from biological sequence data?— a case study. Journal of Computational Biology, 8(5), 493-521. https://doi.org/10.1089/106652701753216512

This paper investigates whether Chomsky-like grammar representations are useful for learning cost-effective, comprehensible predictors of members of biological sequence families. The Inductive Logic Programming (ILP) Bayesian approach to learning fr... Read More about Are grammatical representations useful for learning from biological sequence data?— a case study.

Developing a logical model of yeast metabolism (2001)
Journal Article
Reiser, P., King, R., Muggleton, S., Bryant, C., Oliver, S., & Kell, D. (2001). Developing a logical model of yeast metabolism

With the completion of the sequencing of genomes of increasing numbers of organisms, the focus of biology is moving to determining the role of these genes (functional
genomics). To this end it is useful to view the cell as a
biochemical machine: it... Read More about Developing a logical model of yeast metabolism.

Combining inductive logic programming, active learning and robotics to discover the function of genes (2001)
Journal Article
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

The paper is addressed to AI workers with an interest in biomolecular genetics and also to biomolecular geneticists interested in what AI tools may do for them. The authors are engaged in a collaborative enterprise aimed at partially automating some... Read More about Combining inductive logic programming, active learning and robotics to discover the function of genes.