@article{bf18957263fa425da26b96c3f0c425d9,
title = "Simultaneous Integration of Multi-omics Data Improves the Identification of Cancer Driver Modules",
abstract = "Integrating different data types to answer biological questions is a challenging problem, which can, however, provide stronger insights than using each dataset separately. ModulOmics is a statistical framework to integrate multiple omics data types and various statistical tests into one probabilistic model, with the aim of identifying functionally connected modules. It simultaneously (rather than sequentially)optimizes all tests and efficiently searches the large candidates space with a two-step optimization procedure. Across cancer types, ModulOmics identifies key modules representing cancer-related mechanisms.",
keywords = "cancer, cancer drivers, cancer pathways, data integration, driver modules, integer linear programming, mutual exclusivity, simultaneous optimization",
author = "Dana Silverbush and Simona Cristea and Gali Yanovich-Arad and Tamar Geiger and Niko Beerenwinkel and Roded Sharan",
note = "Publisher Copyright: {\textcopyright} 2019 Elsevier Inc.",
year = "2019",
month = may,
day = "22",
doi = "10.1016/j.cels.2019.04.005",
language = "אנגלית",
volume = "8",
pages = "456--466.e5",
journal = "Cell Systems",
issn = "2405-4712",
publisher = "Cell Press",
number = "5",
}