In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@inproceedings{Kakas:2006:10.1007/s10994-006-8988-x,
author = {Kakas, A and Tamaddoni, Nezhad A and Muggleton, S and Chaleil, R},
doi = {10.1007/s10994-006-8988-x},
pages = {209--230},
publisher = {Springer},
title = {Application of abductive ILP to learning metabolic network inhibition from temporal data},
url = {http://dx.doi.org/10.1007/s10994-006-8988-x},
year = {2006}
}
TY - CPAPER
AB - In this paper we use a logic-based representation and a combination of Abduction and Induction to model inhibition in metabolic networks. In general, the integration of abduction and induction is required when the following two conditions hold. Firstly, the given background knowledge is incomplete. Secondly, the problem must require the learning\r\nof general rules in the circumstance in which the hypothesis language is disjoint from the observation language. Both these conditions hold in the application considered in this paper. Inhibition is very important from the therapeutic point of view since many substances designed to be used as drugs can have an inhibitory effect on other enzymes. Any system able to predict the inhibitory effect of substances on the metabolic network would therefore be very useful in assessing the potential harmful side-effects of drugs. In modelling the phenomenon\r\nof inhibition in metabolic networks, background knowledge is used which describes the network topology and functional classes of inhibitors and enzymes. This background knowledge, which represents the present state of understanding, is incomplete. In order to overcome this incompleteness hypotheses are considered which consist of a mixture of specific inhibitions of enzymes (ground facts) together with general (non-ground) rules which predict classes of enzymes likely to be inhibited by the toxin. The foreground examples are derived from\r\nin vivo experiments involving NMR analysis of time-varying metabolite concentrations in rat urine following injections of toxins. The modelÆs performance is evaluated on training and test sets randomly generated from a real metabolic network. It is shown that even in\r\nthe case where the hypotheses are restricted to be ground, the predictive accuracy increases with the number of training examples and in all cases exceeds the default (majority class).\r\nExperimental results also suggest that when sufficient training data is provided
AU - Kakas,A
AU - Tamaddoni,Nezhad A
AU - Muggleton,S
AU - Chaleil,R
DO - 10.1007/s10994-006-8988-x
EP - 230
PB - Springer
PY - 2006///
SN - 0885-6125
SP - 209
TI - Application of abductive ILP to learning metabolic network inhibition from temporal data
UR - http://dx.doi.org/10.1007/s10994-006-8988-x
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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