A time-delayed information-theoretic approach to the reverse engineering of gene regulatory networks using apache spark

Document Type

Conference Proceeding

Publication Date

7-2-2017

Abstract

Elucidating gene regulatory networks (GRNs) is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer or reconstruct gene regulatory networks from expression data. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here we present a new algorithm for reverse engineering (inferring) gene regulatory networks on a computer cluster in a cloud environment. The algorithm, implemented in Apache Spark, employs an information-theoretic approach to infer GRNs from time-series gene expression data. Experimental results show that our Spark program is much faster than an existing tool while achieving the same prediction accuracy.

Identifier

85048115826 (Scopus)

ISBN

[9781538619551]

Publication Title

Proceedings 2017 IEEE 15th International Conference on Dependable Autonomic and Secure Computing 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress Dasc Picom Datacom Cyberscitec 2017

External Full Text Location

https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2017.179

First Page

1106

Last Page

1113

Volume

2018-January

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