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Personalized Network Modeling of the Pan-Cancer Patient and Cell Line Interactome
[article]
2019
bioRxiv
pre-print
Purpose: Personalized network inference on diverse clinical and in vitro model systems across cancer types can be used to delineate specific regulatory mechanisms, uncover drug targets and pathways, and develop individualized predictive models in cancer. Datasets and methods: We developed TransPRECISE, a multi-scale Bayesian network modeling framework, to analyze the pan-cancer patient and cell line interactome to identify differential and conserved intra-pathway activities, globally assess
doi:10.1101/806596
fatcat:t2snhz4h65hy3nn5ffcvoszlxa