Dr Chris Bryant's Outputs (3)

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.