All Outputs (33)

Pruning methods for rule induction (2017)
Thesis
Othman, O. (2017). Pruning methods for rule induction. (Thesis). University of Salford

Machine learning is a research area within computer science that is mainly concerned with discovering regularities in data. Rule induction is a powerful technique used in machine learning wherein the target concept is represented as a set of rules. T... Read More about Pruning methods for rule induction.

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.

Preceding rule induction with instance reduction methods (2013)
Conference Proceeding
Othman, O., & Bryant, C. (2013). Preceding rule induction with instance reduction methods. In P. Perner (Ed.), Proceedings of the 9th International Conference on Machine Learning and Data Mining in Pattern Recognition (209-218). https://doi.org/10.1007/978-3-642-39712-7_16

A new prepruning technique for rule induction is presented which applies instance reduction before rule induction. An empirical evaluation records the predictive accuracy and size of rule-sets generated from 24 datasets from the UCI Machine Learning... Read More about Preceding rule induction with instance reduction methods.

Comparing the performance of object and object relational database systems on objects of varying complexity (2012)
Conference Proceeding
Kalantari, R., & Bryant, C. (2012). Comparing the performance of object and object relational database systems on objects of varying complexity. In L. MacKinnon (Ed.), Proceedings of the 27th British National Conference on Databases. https://doi.org/10.1007/978-3-642-25704-9_8

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.

A sequence-length sensitive approach to learning biological grammars using inductive logic programming (2011)
Thesis
Mamer, T. (2011). A sequence-length sensitive approach to learning biological grammars using inductive logic programming. (Thesis). The Robert Gordon University

This thesis aims to investigate if the ideas behind compression principles, such as the Minimum Description Length, can help us to improve the process of learning biological grammars from protein sequences using Inductive Logic Programming (ILP). Con... Read More about A sequence-length sensitive approach to learning biological grammars using inductive logic programming.

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.

An Inductive Logic Programming Approach to Learning which uORFs Regulate Gene Expression (2008)
Thesis
Selpi. (2008). An Inductive Logic Programming Approach to Learning which uORFs Regulate Gene Expression. (Thesis). The Robert Gordon University

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 het fully understood. In order to get a compl... Read More about An Inductive Logic Programming Approach to Learning which uORFs Regulate Gene Expression.

Inferring the function of genes from synthetic lethal mutations (2008)
Conference Proceeding
Ray, O., & Bryant, C. (2008). Inferring the function of genes from synthetic lethal mutations. In F. Xhafa, & L. Barolli (Eds.), Complex, Intelligent and Software Intensive Systems (667-671). https://doi.org/10.1109/CISIS.2008.124

Techniques for detecting synthetic lethal mutations in double gene deletion experiments are emerging as powerful tool for analysing genes in parallel or overlapping pathways with a shared function. This paper introduces a logic-based approach that us... Read More about Inferring the function of genes from synthetic lethal mutations.

L-modified ILP evaluation functions for positive-only biological grammar learning (2008)
Book Chapter
Mamer, T., Bryant, C., & McCall, J. (2008). L-modified ILP evaluation functions for positive-only biological grammar learning. In F. Zelezny, & N. Lavrac (Eds.), Inductive logic programming (176-191). Berlin / Heidelberg, Germany: Springer. https://doi.org/10.1007/978-3-540-85928-4_16

We identify a shortcoming of a standard positive-only clause evaluation function within the context of learning biological grammars. To overcome this shortcoming we propose L-modification, a modification to this evaluation function such that the leng... Read More about L-modified ILP evaluation functions for positive-only biological grammar learning.

A first step towards learning which uORFs regulate gene expression (2007)
Book Chapter
Selp, S., Bryant, C., Kemp, G., & Cvijovic, M. (2007). A first step towards learning which uORFs regulate gene expression. In R. Hofestadt, & T. Topel (Eds.), Annual collection of articles of the Journal of Integrative Bioinformatics(JIB) (137-150). Aachen, Germany: Shaker Verlag

An ILP refinement operator for biological grammar learning (2007)
Book Chapter
Fredouille, D., Bryant, C., Jayawickreme, C., Jupe, S., & Topp, S. (2007). An ILP refinement operator for biological grammar learning. In S. Muggleton, R. Otero, & A. Tamaddoni-Nezhad (Eds.), Inductive logic programming (214-228). Berlin / Heidelberg, Germany: Springer. https://doi.org/10.1007/978-3-540-73847-3_24

We are interested in using Inductive Logic Programming (ILP) to infer grammars representing sets of biological sequences. We call these biological grammars. ILP systems are well suited to this task in the sense that biological grammars have been repr... Read More about An ILP refinement operator for biological grammar learning.

Pertinent background knowledge for learning protein grammars (2006)
Book Chapter
Bryant, C., Fredouille, D., Wilson, A., Jayawickreme, C., Jupe, S., & Topp, S. (2006). Pertinent background knowledge for learning protein grammars. In J. Fürnkranz, T. Scheffer, & M. Spiliopoulou (Eds.), Machine learning: ECML 2006 (54-65). Berlin / Heidelberg, Germany: Springer. https://doi.org/10.1007/11871842_10

We are interested in using Inductive Logic Programming(ILP) to infer grammars representing sets of protein sequences. ILP takes as input both examples and background knowledge predicates. This work is a first step in optimising the choice of backgrou... Read More about Pertinent background knowledge for learning protein grammars.

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.

A parser for the efficient induction of biological grammars (2005)
Presentation / Conference
Bryant, C., & Fredouille, D. (2005, August). A parser for the efficient induction of biological grammars. Presented at 15th International Conference on Inductive Logic Programming, Bonne, Germany

Speeding up parsing of biological context-free grammars (2005)
Book Chapter
Fredouille, D., & Bryant, C. (2005). Speeding up parsing of biological context-free grammars. In A. Apostolico, M. Crochemore, & K. Park (Eds.), Proceedings of the 16th Annual Symposium on Combinatorial pattern matching (241-256). Berlin / Heidelberg, Germany: Springer. https://doi.org/10.1007/11496656_21

Grammars have been shown to be a very useful way to model biological sequences families. As both the quantity of biological sequences and the complexity of the biological grammars increase, generic and efficient methods for parsing are needed. We con... Read More about Speeding up parsing of biological context-free grammars.

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.