All Outputs (9)

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.

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.

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.

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.

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.