Gene Regulatory Network Inference with Evolution Strategies and Sparse Matrix Representation

Youchuan Wang, Chilukuri K. Mohan

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

Abstract

Gene Regulatory Networks (GRNs) represent the functional interactions between genes, such as the expression of one gene enhancing the subsequent expression of another gene. GRNs can be inferred from observations of the expressions of various genes over time. This task is difficult, due to the complex interactions among collections of genes, as well as the inherent noise in the observable data. In realistic genomes with thousands of genes, millions of gene-pairs exist, although the actual number of possible interactions may be considerably smaller, since each gene may affect only a small number of other genes. This motivates the application of sparse learning algorithms for GRN inference. In particular, we explore evolutionary algorithms, and their applications with sparse matrix representations. Our approach can speed up the optimization process and find good solutions, uncovering the underlying GRNs. We study evolution strategies, particle swarm optimization, and a greedy algorithm, and compare their performance in terms of solution quality and computational effort required for GRN inference.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
EditorsIllhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2105-2112
Number of pages8
ISBN (Electronic)9781728118673
DOIs
StatePublished - Nov 2019
Event2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States
Duration: Nov 18 2019Nov 21 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

Conference2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
CountryUnited States
CitySan Diego
Period11/18/1911/21/19

Keywords

  • evolution strategies
  • evolutionary algorithms
  • gene regulatory network
  • sparse learning

ASJC Scopus subject areas

  • Biochemistry
  • Biotechnology
  • Molecular Medicine
  • Modeling and Simulation
  • Health Informatics
  • Pharmacology (medical)
  • Public Health, Environmental and Occupational Health

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  • Cite this

    Wang, Y., & Mohan, C. K. (2019). Gene Regulatory Network Inference with Evolution Strategies and Sparse Matrix Representation. In I. Yoo, J. Bi, & X. T. Hu (Eds.), Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 (pp. 2105-2112). [8983230] (Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM47256.2019.8983230