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 usin...

• Type: Conference
• Year: 2005
• Where: ILP
• Authors: Stephen Muggleton
• Keywords: ILP 2005, Machine Learning, Network Involve Metabolite, Study Kegg Pathways