Abstract
Abstract This paper outlines some aspects of a steroid-profiling expert system under development as an alternative to the conventional chemometric approach to metabolic profiling which uses only statistical pattern recognition. Deep knowledge is being structured as a causal model and subjective probabilistic reasoning over the causal model (using interval-valued probabilities) is attempted by means of a graph-theoretic separation criterion for assessing conditional independence. An approach for mapping chromatographic features to corresponding linguistic descriptions is discussed.
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