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Abduction and induction for modelling inhibition in metabolic networks

Alireza Tamaddoni-Nezhad, Stephen Muggleton

Conference or Workshop Paper
4th International Conference on Machine Learning and Applications
Proceedings of the Fourth International Conference on Machine Learning and Applications
December, 2005
IEEE Computer Society
ISBN 0-7695-2495-8
DOI 10.1109/ICMLA.2005.6

This paper describes the use of a mixture of abduction

and induction for the problem of identifying

the effects of toxins in metabolic networks. Background

knowledge is used which describes network

topology and functional classes of enzymes.

This background knowledge, which represents the

present state of understanding, is incomplete. In

order to overcome this incompleteness hypotheses

are entertained which consist of a mixture of

specific inhibitions of enzymes (ground facts) together

with general rules which predict classes of

enzymes likely to be inhibited by the toxin (nonground).

The foreground examples were derived

from in vivo experiments involving NMR analysis

of time-varying metabolite concentrations in rat

urine following injections of toxin. Hypotheses

about inhibition are built using Progol5.0 and predictive

accuracy is assessed for both the ground and

the non-ground cases.

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