ECAI 2004 Conference Paper

[full paper]

Learning qualitative metabolic models

George M. Coghill, Simon M. Garrett, Ross D. King

The ability to learn a model of a system from observations of the system and background knowledge is central to intelligence, and the automation of the process is a key research goal of Artificial Intelligence. We present a model-learning system, developed for application to scientific discovery problems, where the models are scientific hypotheses and the observations are experiments. The learning system, {\sc Qoph} learns the {\it structural} relationships between the observed variables, known to be a hard problem. {\sc Qoph} has been shown capable of learning models with hidden (unmeasured) variables, under different levels of noise, and from qualitative or quantitative input data.

Keywords: Model learning, Qualtitative Reasoning, ILP, Computational Biology

Citation: George M. Coghill, Simon M. Garrett, Ross D. King: Learning qualitative metabolic models. In R.López de Mántaras and L.Saitta (eds.): ECAI2004, Proceedings of the 16th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2004, pp.445-449.