Forbes S. A. / Oxford University Press / Kenfield S. A. / Creative Commons / /
Country
United States / / /
Event
Product Recall / Product Issues / /
Facility
University Avenue / University of Wisconsin / /
IndustryTerm
replication machinery / post-processing step / systematic search / data download portal / mutation site / /
MedicalCondition
ovarian cancer / well-studied cancer / cancer Keegan D. Korthauer1 / multidimensional cancer / colorectal cancers / small-cell lung cancer / each cancer / complete cancer / lung adenocarcinoma / breast and colorectal cancer / cancers / tumor / squamous cell lung cancer / INTRODUCTION Cancer / breast cancers / available ovarian and squamous cell lung cancer / cancer / Ovarian cancer MADGiC / human cancer / single cancer / lung cancer / non smoking-related cancer / different cancer / /
Organization
National Institute of Health / Department of Statistics / Department of Biostatistics and Medical Informatics / Oxford University / University of Wisconsin / /
Person
Simon / Zg / Nat / Fraction / Youn / Alfonso Valencia / /
Position
driver / Editor / background mutation model / external estimates / Author / mutation-type and nucleotide context-specific rate parameters pm / putative driver / mutation type pm / /
Product
Parameter estimation / /
ProvinceOrState
New Jersey / Wisconsin / North Dakota / /
RadioStation
Lung Oncodrive FM / /
Technology
genomics / Bioinformatics / DNA Chip / gene expression / Simulation / drug development / /
Bioinformatics Advance Access published January 7, 2015 MADGiC: a model-based approach for identifying driver genes in cancer Keegan D. Korthauer1 and Christina Kendziorski2