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Bayesian evolutionary hypergraph learning for predicting cancer clinical outcomes
2014
Journal of Biomedical Informatics
Predicting the clinical outcomes of cancer patients is a challenging task in biomedicine. A personalized and refined therapy based on predicting prognostic outcomes of cancer patients has been actively sought in the past decade. Accurate prognostic prediction requires higher-order representations of complex dependencies among genetic factors. However, identifying the co-regulatory roles and functional effects of genetic interactions on cancer prognosis is hindered by the complexity of the
doi:10.1016/j.jbi.2014.02.002
pmid:24524888
fatcat:o5mod3y56reihfx7kwxbxgy3bq