Gene Regulatory Network Inference through Link Prediction using Graph Neural Network

Research output: Chapter in Book/Report/Conference proceedingConference proceedingpeer-review

Abstract

Gene Regulatory Networks (GRNs) depict the causal regulatory interactions between transcription factors (TFs) and their target genes [2], where TFs are proteins that regulate gene transcription. GRN plays a vital role in explaining gene function, which helps to identify and prioritize the candidate genes for functional analysis [3]. Currently, high-dimensional transcriptome datasets are produced from high-throughput sequencing techniques, such as microarray and RNA-Seq. These techniques can capture the differences in the expression of thousands of genes at once. Through these wet-lab experiments, studying the interconnections among a large number of genes or TFs at a network level is challenging [4]. Therefore, one of the important topics in computational biology is the inference of GRNs from high-dimensional gene expression data through statistical and machine learning approaches [2].
Original languageEnglish
Title of host publication2022 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)978-1-6654-7029-2
ISBN (Print)978-1-6654-7030-8
DOIs
Publication statusPublished - 19 Jan 2023
Event2022 IEEE Signal Processing in Medicine and Biology Symposium - Philadelphia, United States
Duration: 3 Dec 20223 Dec 2022
https://www.ieeespmb.org/2022/

Publication series

NameIEEE Signal Processing in Medicine and Biology Symposium (SPMB)
PublisherIEEE
ISSN (Print)2372-7241
ISSN (Electronic)2473-716X

Conference

Conference2022 IEEE Signal Processing in Medicine and Biology Symposium
Abbreviated titleSPMB 2022
Country/TerritoryUnited States
CityPhiladelphia
Period3/12/223/12/22
Internet address

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Proteins
  • Sequential analysis
  • Machine learning
  • Signal processing
  • Graph neural networks
  • Functional analysis
  • Biology

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