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

Tel Aviv University

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

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

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

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

All Science Journal Classification (ASJC) codes

Pathology and Forensic Medicine
Histology
Cell Biology

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Cite this

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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.",

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author = "Dana Silverbush and Simona Cristea and Gali Yanovich-Arad and Tamar Geiger and Niko Beerenwinkel and Roded Sharan",

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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

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DO - 10.1016/j.cels.2019.04.005

M3 - مقالة

C2 - 31103572

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

UN SDGs

Keywords

All Science Journal Classification (ASJC) codes

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