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Extracting significant sample-specific cancer mutations using their protein interactions
2014
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
We present a joint analysis method for mutation and gene expression data employing information about proteins that are highly interconnected at the level of protein to protein (pp) interactions, which we apply to the TCGA Acute Myeloid Leukemia (AML) dataset. Given the low incidence of most mutations in virtually all cancer types, as well as the significant inter-patient heterogeneity of the mutation landscape, determining the true causal mutations in each individual patient remains one of the
pmid:24297530
fatcat:y7oogqy6zrcchijl5zfeiuxvom