We describe an application of Abductive Logic Programming
(ALP) to the analysis of an important class of DNA microarray
experiments. These experiments measure di erences in expression levels
of whole genomes in di ering environmental conditions and/or after
deletion or overexpression of one or more genes. Their aim is to obtain
insights about gene interactions and gene pathways. We develop
an ALP theory that provides a simple and general model of how gene
interactions can cause changes in observable expression levels of genes.
Input to the procedure are the observed microarray results; output are
hypotheses about possible gene interactions that explain the observed
e ects. A key feature of the model are parameters that encode di erent
biological assumptions and provide a means of constraining the search
for possible hypotheses. We have applied and evaluated our approach on
microarray experiments on M.tuberculosis and on S.cerevisiae (yeast).
Comparison of inferred hypotheses against known gene regulation networks
and known gene functions in the biological literature provide a
form of independent validation of the model.