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Gene expression prediction based on neighbour connection neural network utilizing gene interaction graphs

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Listed:
  • Xuanyu Li
  • Xuan Zhang
  • Wenduo He
  • Deliang Bu
  • Sanguo Zhang

Abstract

Having observed that gene expressions have a correlation, the Library of Integrated Network-based Cell-Signature program selects 1000 landmark genes to predict the remaining gene expression value. Further works have improved the prediction result by using deep learning models. However, these models ignore the latent structure of genes, limiting the accuracy of the experimental results. We therefore propose a novel neural network named Neighbour Connection Neural Network(NCNN) to utilize the gene interaction graph information. Comparing to the popular GCN model, our model incorperates the graph information in a better manner. We validate our model under two different settings and show that our model promotes prediction accuracy comparing to the other models.

Suggested Citation

  • Xuanyu Li & Xuan Zhang & Wenduo He & Deliang Bu & Sanguo Zhang, 2023. "Gene expression prediction based on neighbour connection neural network utilizing gene interaction graphs," PLOS ONE, Public Library of Science, vol. 18(2), pages 1-18, February.
    • Handle: RePEc:plo:pone00:0281286
    DOI: 10.1371/journal.pone.0281286
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    References listed on IDEAS

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    1. Brian T. Wilhelm & Samuel Marguerat & Stephen Watt & Falk Schubert & Valerie Wood & Ian Goodhead & Christopher J. Penkett & Jane Rogers & Jürg Bähler, 2008. "Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution," Nature, Nature, vol. 453(7199), pages 1239-1243, June.
    2. Jacob M Zahn & Suresh Poosala & Art B Owen & Donald K Ingram & Ana Lustig & Arnell Carter & Ashani T Weeraratna & Dennis D Taub & Myriam Gorospe & Krystyna Mazan-Mamczarz & Edward G Lakatta & Kenneth , 2007. "AGEMAP: A Gene Expression Database for Aging in Mice," PLOS Genetics, Public Library of Science, vol. 3(11), pages 1-12, November.
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