Abstract
In several recent papers ILP has been applied to Systems Biology problems, in which it has been used to fill gaps in the descriptions of biological networks. In the present paper we describe two new applications of this type in the area of plant biology. These applications are of particular interest to the agrochemical industry in which improvements in plant strains can have benefits for modelling crop development. The background knowledge in these applications is extensive and is derived from public databases in a Prolog format using a new system called Ondex (developers BBSRC Rothamsted). In this paper we explore the question of how much of this background knowledge it is beneficial to include, taking into account accuracy increases versus increases in learning time. The results indicate that relatively shallow background knowledge is needed to achieve maximum accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Syngenta Ltd., http://www.syngenta.com/en/index.html
Syngenta University Innovation Centre, http://www3.imperial.ac.uk/syngenta-uic
Chen, J., Muggleton, S.H., Santos, J.: Learning probabilistic logic models from probabilistic examples. Machine Learning 73(1), 55–85 (2008), 10.1007/s10994-008-5076-4
Mueller, L.A., et al.: The SOL Genomics Network. A Comparative Resource for Solanaceae Biology and Beyond. Plant Physiology 138(3), 1310–1317 (2005)
Goble, C., Stevens, R.: State of the nation in data integration for bioinformatics. Journal of Biomedical Informatics 41(5), 687–693 (2008)
Kitano, H.: Computational systems biology. Nature 420, 206–210 (2002)
Kohler, J., Baumbach, J., Taubert, J., Specht, M., Skusa, A., Ruegg, A., Rawlings, C., Verrier, P., Philippi, S.: Graph-based analysis and visualization of experimental results with ONDEX. Bioinformatics 22(11), 1383–1390 (2006)
Kruskal, J.B.: On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical Society 7(1), 48–50 (1956)
LycoCyc. Solanum lycopersicum database, http://solcyc.solgenomics.net//LYCO/
Muggleton, S.H., Bryant, C.H.: Theory completion using inverse entailment. In: Cussens, J., Frisch, A.M. (eds.) ILP 2000. LNCS (LNAI), vol. 1866, pp. 130–146. Springer, Heidelberg (2000)
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., Kanehisa, M.: KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucl. Acids Res. 27(1), 29–34 (1999)
Rubtsov, D.V., Waterman, C., Currie, R.A., Waterfield, C., Salazar, J.D., Wright, J., Griffin, J.L.: Application of a bayesian deconvolution approach for high-resolution 1h nmr spectra to assessing the metabolic effects of acute phenobarbital exposure in liver tissue. Analytical Chemistry 82(11), 4479–4485 (2010)
Tamaddoni-Nezhad, A., Chaleil, R., Kakas, A., Muggleton, S.H.: Application of abductive ILP to learning metabolic network inhibition from temporal data. Machine Learning 64, 209–230 (2006), doi:10.1007/s10994-006-8988-x
Waterman, C., Currie, R., Cottrell, L., Dow, J., Wright, J., Waterfield, C., Griffin, J.: An integrated functional genomic study of acute phenobarbital exposure in the rat. BMC Genomics 11(1), 9 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Muggleton, S.H. et al. (2011). Variation of Background Knowledge in an Industrial Application of ILP. In: Frasconi, P., Lisi, F.A. (eds) Inductive Logic Programming. ILP 2010. Lecture Notes in Computer Science(), vol 6489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21295-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-21295-6_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21294-9
Online ISBN: 978-3-642-21295-6
eBook Packages: Computer ScienceComputer Science (R0)