Machine Learning for Systems Biology


In this paper we survey work being conducted at Imperial College on the use of machine learning to build Systems Biology models of the effects of toxins on biochemical pathways. Several distinct, and complementary modelling techniques are being explored. Firstly, work is being conducted on applying Support-Vector ILP (SVILP) as an accurate means of screening high-toxicity molecules. Secondly, Bayes’ networks have been machine-learned to provide causal maps of the effects of toxins on the network of metabolic reactions within cells. The data were derived from a study on the effects of hydrazine toxicity in rats. Although the resultant network can be partly explained in terms of existing KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway descriptions, several of the strong dependencies in the Bayes’ network involve metabolite pairs with high separation in KEGG. Thirdly, in a complementary study KEGG pathways are being used as background knowledge for explaining the same data using a model constructed using Abductive ILP, a logic-based machine learning technique. With a binary prediction model (up/down regulation) cross validation results show that even with a restricted number of observed metabolites high predictive accuracy (80-90%) is achieved on unseen metabolite concentrations. Further increases in accuracy are achieved by allowing discovery of general rules from additional literature data on hydrazine inhibition. Ongoing work is aimed at formulating probabilistic logic models which combine the learned Bayes’ network and ILP models.

DOI: 10.1007/11536314_27

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@inproceedings{Muggleton2005MachineLF, title={Machine Learning for Systems Biology}, author={Stephen Muggleton}, booktitle={ILP}, year={2005} }