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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.

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.

Learning Chomsky-like grammars for biological sequence families (2000)
Conference Proceeding
Muggleton, S., Bryant, C., & Srinivasan, A. (2000). Learning Chomsky-like grammars for biological sequence families. In P. Langley (Ed.), Proceedings of the 17th International Conference on Machine Learning (631-638)

This paper presents a new method of measuring performance when positives are rare and investigates whether Chomsky-like grammar representations are useful for learning accurate comprehensible predictors of members of biological sequence families. The... Read More about Learning Chomsky-like grammars for biological sequence families.

Measuring performance when positives are rare: relative advantage versus predictive accuracy - a biological case-study (2000)
Conference Proceeding
Muggleton, S., Bryant, C., & Srinivasan, A. (2000). Measuring performance when positives are rare: relative advantage versus predictive accuracy - a biological case-study. In R. de Mántaras, & E. Plaza (Eds.), Machine learning: ECML 2000: 11th European conference on machine learning, Barcelona, Catalonia, Spain, May 31-June 2 2000 (300-312)

This paper presents a new method of measuring performance when positives are rare and investigates whether Chomsky-like grammar representations are useful for learning accurate comprehensible predictors of members of biological sequence families. The... Read More about Measuring performance when positives are rare: relative advantage versus predictive accuracy - a biological case-study.

Theory completion using inverse entailment (2000)
Book Chapter
Muggleton, S., & Bryant, C. (2000). Theory completion using inverse entailment. In J. Cussens, & A. Frisch (Eds.), Inductive Logic Programming (130-146). London, UK: Springer. https://doi.org/10.1007/3-540-44960-4_8

The main real-world applications of Inductive Logic Programming (ILP) to date involve the "Observation Predicate Learning" (OPL) assumption, in which both the examples and hypotheses define the same predicate. However, in both scientific discovery... Read More about Theory completion using inverse entailment.

Combining active learning with inductive logic programming to close the loop in machine learning (1999)
Book Chapter
programming to close the loop in machine learning. In S. Colton (Ed.), Proceedings of AISB'99 Symposium on AI and Scientific Creativity (59-64). The Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB)

Machine Learning (ML) systems that produce human-comprehensible hypotheses from data are typically open loop, with no direct link between the ML system and the collection of data. This paper describes the alternative, it Closed Loop Machine Learning.... Read More about Combining active learning with inductive logic programming to close the loop in machine learning.

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.

Transforming general program proofs: a meta interpreter which expands negative literals (1997)
Presentation / Conference
West, M., Bryant, C., & McCluskey, T. (1997, July). Transforming general program proofs: a meta interpreter which expands negative literals. Presented at 7th International Workshop on Logic Program Synthesis and Transformation, Leuven, Belgium

This paper provides a method for generating a proof tree from an instance and a general logic program viz one which includes negative literals. The method differs from previous work in the field in that negative literals are first unfolded and then... Read More about Transforming general program proofs: a meta interpreter which expands negative literals.

Data mining via ILP: The application of progol to a (1997)
Book Chapter
Bryant, C. (1997). Data mining via ILP: The application of progol to a. In N. Lavrac, & S. Dzeroski (Eds.), Inductive logic programming (85-92). Berlin / Heidelberg, Germany: Springer. https://doi.org/10.1007/3540635149_37

As far as this author is aware, this is the first paper to describe the application of Progol to enantioseparations. A scheme is proposed for data mining a relational database of published enantioseparations using Progol. The application of the schem... Read More about Data mining via ILP: The application of progol to a.

The validation of formal specifications of requirements (1996)
Conference Proceeding
McCluskey, T., Porteous, J., Bryant, C., & West, M. (1996). The validation of formal specifications of requirements. . https://doi.org/10.14236/ewic/FA1996.14

We review the approaches put forward to validate formal specifications of requirements, drawing a parallel with research into the validation of knowledge bases. Using an industrial-scale case study we describe a partially implemented, integrated envi... Read More about The validation of formal specifications of requirements.

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.

Discovering knowledge hidden in a chemical database using a commercially available data mining tool (1995)
Presentation / Conference
Bryant, C., Adam, A., Taylor, D., Conroy, G., & Rowe, R. (1995, February). Discovering knowledge hidden in a chemical database using a commercially available data mining tool. Presented at IEE Colloquium on Knowledge Discovery in Databases, London, UK

Describes DataMariner, a commercially available tool that is designed to facilitate the discovery of knowledge hidden in databases. The potential of the tool for scientific applications is illustrated via a case study. This is both the first applicat... Read More about Discovering knowledge hidden in a chemical database using a commercially available data mining tool.

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.