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We
have previously exploited different types of
data and temporal information to determine causality
using a probabilistic learning model and also
an inductive learning method known as Inductive
Logic Programming. The two methods are good
at inferring regulatory relationships and our
use of temporal data provides more confidence
that the relationships learned represent causality.
We are interested in learning regulators of
specific pathways in yeast and humans. We plan
to explore models that combine statistical and
relational learning
methods.
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