Instance-Specific Causal Bayesian Network Learning
This talk describes an instance-specific causal Bayesian network (CBN) learning method that searches the space of CBNs to build a causal model that is specific to an instance (e.g., a patient). The search is guided by attributes of the given instance (e.g., patient symptoms, signs, lab results, and genotype). We describe the results of applying the method to molecular cancer data to estimate the gene alterations (e.g., gene mutations) that are driving the cancerous behavior of individual tumors, which are the instances in this application. Follow-up biological experiments provide support that the method is able to identify new genomic drivers of cancer.
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