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Cancer progression modeling using static sample data
2014
Genome Biology
As molecular profiling data continue to accumulate, the design of integrative computational analyses that can provide insights into the dynamic aspects of cancer progression becomes feasible. Here, we present a novel computational method for the construction of cancer progression models based on the analysis of static tumor samples. We demonstrate the reliability of the method with simulated data, and describe the application to breast cancer data. Our findings support a linear, branching model
doi:10.1186/s13059-014-0440-0
pmid:25155694
pmcid:PMC4196119
fatcat:o56keqrbwncbhdgznj4ycbswfm