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

Yasser Abduallah, Jason T.L. Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - 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
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1106-1113
Number of pages8
ISBN (Electronic)9781538619551
DOIs
StatePublished - Mar 29 2018
Event15th IEEE 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 - Orlando, United States
Duration: Nov 6 2017Nov 11 2017

Publication series

NameProceedings - 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
Volume2018-January

Other

Other15th IEEE 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
CountryUnited States
CityOrlando
Period11/6/1711/11/17

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Health Informatics
  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Computer Science Applications
  • Information Systems

Keywords

  • Big data
  • Map reduce
  • Network inference
  • Spark
  • Systems biology

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