All Outputs (11)

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.

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.

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.

Functional genomic hypothesis generation and experimentation by a robot scientist (2004)
Journal Article
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

The question of whether it is possible to automate the scientific process is of both great theoretical interest and increasing practical importance because, in many scientific areas, data are being generated much faster than they can be effectively a... Read More about Functional genomic hypothesis generation and experimentation by a robot scientist.

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.

Knowledge discovery in databases: application to chromatography (1998)
Journal Article
Bryant, C., & Rowe, R. (1998). Knowledge discovery in databases: application to chromatography. Trends in Analytical Chemistry, 17(1), 18-24. https://doi.org/10.1016/S0165-9936%2897%2900094-0

This paper reviews emerging computer techniques for discovering knowledge from databases and their application to various sets of separation data. The data-sets include the separation of a diverse range of analytes using either liquid, gas or ion chr... Read More about Knowledge discovery in databases: application to chromatography.

Using inductive logic programming to discover knowledge hidden in chemical data (1998)
Journal Article
Bryant, C., Adam, A., Taylor, D., & Rowe, R. (1998). Using inductive logic programming to discover knowledge hidden in chemical data. Chemometrics and Intelligent Laboratory Systems, 36(2), 111-123. https://doi.org/10.1016/S0169-7439%2897%2900023-3

This paper demonstrates how general purpose tools from the field of Inductive Logic Programming (ILP) can be applied to analytical chemistry. As far as these authors are aware, this is the first published work to describe the application of the ILP t... Read More about Using inductive logic programming to discover knowledge hidden in chemical data.

Towards an expert system for enantioseparations: induction of rules using machine learning (1996)
Journal Article
Bryant, C., Adam, A., Taylor, D., & Rowe, R. (1996). Towards an expert system for enantioseparations: induction of rules using machine learning. Chemometrics and Intelligent Laboratory Systems, 34(1), 21-40. https://doi.org/10.1016/0169-7439%2896%2900016-0

A commercially available machine induction tool was used in an attempt to automate the acquisition of the knowledge needed for an expert system for enantioseparations by High Performance Liquid Chromatography using Pirkle-type chiral stationary phase... Read More about Towards an expert system for enantioseparations: induction of rules using machine learning.

A review of expert systems for chromatography (1994)
Journal Article
Bryant, C., Adam, A., Taylor, D., & Rowe, R. (1994). A review of expert systems for chromatography. Analytica Chimica Acta, 297(3), 317-347. https://doi.org/10.1016/0003-2670%2894%2900209-6

Expert systems for chromatography are reviewed. A taxonomy is proposed that allows present (and future) expert systems in this area to be classified and facilitates an understanding of their inter-relationship. All the systems are described focusing... Read More about A review of expert systems for chromatography.