MetaLog - Integrated Machine Learning of Metabolic Networks
applied to Predictive Toxicology
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[On-Line Resources] [Poster]
Aims:
The goal of this research programme is to develop an industrial-strength
tool, MetaLog, for modelling disease pathways and predicting toxicity using
machine learned models of proteins and metabolic activity. MetaLog
will be based on a combination of machine learning and uncertainty reasoning.
MetaLog will be developed around problems involving pathway modelling and
predicting the toxicity of a variety of compounds of interest to the industrial
partners. Predictive Toxicology is a vital area for the pharmaceutical industry
(related to Safety Assessment) and can be viewed as an exemplar for post-genomic
biomedical research. In the pharmaceutical sector predictive toxicology is
important for cutting R&D costs and reducing the attrition rate of compounds
in development. Repeatedly in bioinformatic modelling the best algorithm
often is an ad hoc amalgam of different machine learning algorithms. This
results in the end-user being unable to understand the reasoning involved
in predictions. We therefore identify a need both specifically for modelling
of metabolic activity and generally for bioinformatics based on developing
a formal approach to combine different machine learning approaches and to
make the reasoning accessible to end-users.
The project objectives are:
- to develop a formal framework to combine different machine learning
approaches, namely Bayes' nets and Inductive Logic Programming (ILP) and
to make this reasoning methodology accessible to pharmaceutical end users;
- to use the above developments together with metabonomic and proteomic
data to develop an industrial-strength tool, MetaLog, which automatically
hypothesises missing elements of metabolic networks and provides explanations
and predictions concerning the toxicity of a variety of compounds;
Principal Investigators:
In partnership with:
- GlaxoSmithKline Ltd
- Oxagen Ltd
Links: