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March 2017 A statistical framework for data integration through graphical models with application to cancer genomics
Yuping Zhang, Zhengqing Ouyang, Hongyu Zhao
Ann. Appl. Stat. 11(1): 161-184 (March 2017). DOI: 10.1214/16-AOAS998

Abstract

Recent advances in high-throughput biotechnologies have generated various types of genetic, genomic, epigenetic, transcriptomic and proteomic data across different biological conditions. It is likely that integrating data from diverse experiments may lead to a more unified and global view of biological systems and complex diseases. We present a coherent statistical framework for integrating various types of data from distinct but related biological conditions through graphical models. Specifically, our statistical framework is designed for modeling multiple networks with shared regulatory mechanisms from heterogeneous high-dimensional datasets. The performance of our approach is illustrated through simulations and its applications to cancer genomics.

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Yuping Zhang. Zhengqing Ouyang. Hongyu Zhao. "A statistical framework for data integration through graphical models with application to cancer genomics." Ann. Appl. Stat. 11 (1) 161 - 184, March 2017. https://doi.org/10.1214/16-AOAS998

Information

Received: 1 February 2016; Revised: 1 September 2016; Published: March 2017
First available in Project Euclid: 8 April 2017

zbMATH: 1366.62249
MathSciNet: MR3634319
Digital Object Identifier: 10.1214/16-AOAS998

Rights: Copyright © 2017 Institute of Mathematical Statistics

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Vol.11 • No. 1 • March 2017
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