Simultaneous Integration of Multi-omics Data Improves the Identification of Cancer Driver Modules

Dana Silverbush; Simona Cristea; Gali Yanovich-Arad; Tamar Geiger; Niko Beerenwinkel; Roded Sharan

doi:10.1016/j.cels.2019.04.005

Simultaneous Integration of Multi-omics Data Improves the Identification of Cancer Driver Modules

Dana Silverbush^*, Simona Cristea, Gali Yanovich-Arad, Tamar Geiger, Niko Beerenwinkel, Roded Sharan

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

44 Scopus citations

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.

Original language	English
Pages (from-to)	456-466.e5
Journal	Cell Systems
Volume	8
Issue number	5
DOIs	https://doi.org/10.1016/j.cels.2019.04.005
State	Published - 22 May 2019

Funding

Funders	Funder number
Israeli Ministry of Science, Technology
Edmond J. Safra Center for Ethics, Harvard University
Engineering Research Centers
European Research Council	609883
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung	P2EZP2_175139
Tel Aviv University

Keywords

cancer
cancer drivers
cancer pathways
data integration
driver modules
integer linear programming
mutual exclusivity
simultaneous optimization

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.cels.2019.04.005

Cite this

@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",

}

TY - JOUR

T1 - Simultaneous Integration of Multi-omics Data Improves the Identification of Cancer Driver Modules

AU - Silverbush, Dana

AU - Cristea, Simona

AU - Yanovich-Arad, Gali

AU - Geiger, Tamar

AU - Beerenwinkel, Niko

AU - Sharan, Roded

PY - 2019/5/22

Y1 - 2019/5/22

N2 - 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.

AB - 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.

KW - cancer

KW - cancer drivers

KW - cancer pathways

KW - data integration

KW - driver modules

KW - integer linear programming

KW - mutual exclusivity

KW - simultaneous optimization

UR - http://www.scopus.com/inward/record.url?scp=85065748551&partnerID=8YFLogxK

U2 - 10.1016/j.cels.2019.04.005

DO - 10.1016/j.cels.2019.04.005

M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???

AN - SCOPUS:85065748551

SN - 2405-4712

VL - 8

SP - 456-466.e5

JO - Cell Systems

JF - Cell Systems

IS - 5

ER -

Simultaneous Integration of Multi-omics Data Improves the Identification of Cancer Driver Modules

Abstract

Funding

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this