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Machine Learning for Systems Biology

Stephen Muggleton

Conference or Workshop Paper
15th International Conference on Inductive Logic Programming
June, 2005

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.

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