Inferring Gene Co-Expression Networks by Incorporating Prior Protein-Protein Interaction Networks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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  • Meng-Guo Wang
  • Le Ou-Yang
  • Hong Yan
  • Xiao-Fei Zhang

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Original languageEnglish
Pages (from-to)2894-2906
Journal / PublicationIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number5
Online published12 Aug 2022
Publication statusPublished - Sept 2022


Inferring gene co-expression networks from high-throughput gene expression data is an important task in bioinformatics. Many gene networks often exhibit modular structures. Although several Gaussian graphical model-based methods have been developed to estimate gene co-expression networks by incorporating the modular structural prior, none of them takes into account the modular structures captured by the prior networks (e.g., protein interaction networks). In this study, we propose a novel prior network-dependent gene network inference (pGNI) method to estimate gene co-expression networks by integrating gene expression data and prior protein interaction network data. The underlying modular structure is learned from both sets of data. Through simulation studies, we demonstrate the feasibility and effectiveness of our method. We also apply our method to two real datasets. The modular structures in the networks estimated by our method are biological significant.

Research Area(s)

  • Gaussian graphical models, Gene co-expression network inference, modular structures, protein-protein interaction networks

Citation Format(s)