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A neuro-genetic approach for inferring gene regulatory networks from gene expression data

Authors:

Ke ZuoAuthors Info & Claims

ICBRA '22: Proceedings of the 9th International Conference on Bioinformatics Research and Applications

Pages 1 - 5

https://doi.org/10.1145/3569192.3569193

Published: 27 January 2023 Publication History

Abstract

Accurate prediction of gene regulation rules is important for understanding complex life processes. Existing computational algorithms designed for bulk transcriptome datasets typically require a large number of time points to infer gene regulatory networks (GRNs), are suitable for a small number of genes, and cannot efficiently detect potential regulatory relationships. We propose an approach based on a deep learning framework to reconstruct GRNs from bulk transcriptome datasets, assuming that the expression levels of transcription factors involved in gene regulation are strong predictors of the expression of their target genes. The algorithm uses multilayer perceptrons to infer the regulatory relationship between multiple transcription factors and a gene, and uses genetic algorithms to search for the best regulatory gene combination. The results show that our approach is more accurate than other methods for reconstructing gene regulatory networks on real-world and simulated bulk transcriptome gene expression datasets.

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Cited By

Mao GLiu J(2023)An unsupervised deep learning framework for gene regulatory network inference from single-cell expression data2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM58861.2023.10385528(2663-2670)Online publication date: 5-Dec-2023
https://doi.org/10.1109/BIBM58861.2023.10385528

Index Terms

A neuro-genetic approach for inferring gene regulatory networks from gene expression data
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems

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ICBRA '22: Proceedings of the 9th International Conference on Bioinformatics Research and Applications

September 2022

165 pages

ISBN:9781450396868

DOI:10.1145/3569192

Copyright © 2022 ACM.

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Published: 27 January 2023

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ICBRA 2022

ICBRA 2022: 2022 9th International Conference on Bioinformatics Research and Applications

September 18 - 20, 2022

Berlin, Germany

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Cited By

Mao GLiu J(2023)An unsupervised deep learning framework for gene regulatory network inference from single-cell expression data2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM58861.2023.10385528(2663-2670)Online publication date: 5-Dec-2023
https://doi.org/10.1109/BIBM58861.2023.10385528

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