Biostatistics and Medical Informatics
Faculty Advisor: C. David Page
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.
