Deep graph representations embed network information for robust disease marker identification
Abstract
We report that the accurate disease diagnosis and prognosis based on omics data rely on the effective identification of robust prognostic and diagnostic markers that reflect the states of the biological processes underlying the disease pathogenesis and progression. In this article, we present GCNCC, a Graph Convolutional Network-based approach for Clustering and Classification, that can identify highly effective and robust network-based disease markers. Based on a geometric deep learning framework, GCNCC learns deep network representations by integrating gene expression data with protein interaction data to identify highly reproducible markers with consistently accurate prediction performance across independent datasets possibly from different platforms. GCNCC identifies these markers by clustering the nodes in the protein interaction network based on latent similarity measures learned by the deep architecture of a graph convolutional network, followed by a supervised feature selection procedure that extracts clusters that are highly predictive of the disease state. By benchmarking GCNCC based on independent datasets from different diseases (psychiatric disorder and cancer) and different platforms (microarray and RNA-seq), we show that GCNCC outperforms other state-of-the-art methods in terms of accuracy and reproducibility.
- Authors:
-
[1];
[2]
- Texas A & M Univ., College Station, TX (United States)
- Texas A & M Univ., College Station, TX (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
- Publication Date:
- Research Org.:
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1855099
- Report Number(s):
- BNL-222809-2022-JAAM
Journal ID: ISSN 1367-4803
- Grant/Contract Number:
- SC0012704
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Bioinformatics
- Additional Journal Information:
- Journal Volume: 38; Journal Issue: 4; Journal ID: ISSN 1367-4803
- Publisher:
- International Society for Computational Biology - Oxford University Press
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES
Citation Formats
Maddouri, Omar, Qian, Xiaoning, and Yoon, Byung-Jun. Deep graph representations embed network information for robust disease marker identification. United States: N. p., 2021.
Web. doi:10.1093/bioinformatics/btab772.
Maddouri, Omar, Qian, Xiaoning, & Yoon, Byung-Jun. Deep graph representations embed network information for robust disease marker identification. United States. https://doi.org/10.1093/bioinformatics/btab772
Maddouri, Omar, Qian, Xiaoning, and Yoon, Byung-Jun. Thu .
"Deep graph representations embed network information for robust disease marker identification". United States. https://doi.org/10.1093/bioinformatics/btab772. https://www.osti.gov/servlets/purl/1855099.
@article{osti_1855099,
title = {Deep graph representations embed network information for robust disease marker identification},
author = {Maddouri, Omar and Qian, Xiaoning and Yoon, Byung-Jun},
abstractNote = {We report that the accurate disease diagnosis and prognosis based on omics data rely on the effective identification of robust prognostic and diagnostic markers that reflect the states of the biological processes underlying the disease pathogenesis and progression. In this article, we present GCNCC, a Graph Convolutional Network-based approach for Clustering and Classification, that can identify highly effective and robust network-based disease markers. Based on a geometric deep learning framework, GCNCC learns deep network representations by integrating gene expression data with protein interaction data to identify highly reproducible markers with consistently accurate prediction performance across independent datasets possibly from different platforms. GCNCC identifies these markers by clustering the nodes in the protein interaction network based on latent similarity measures learned by the deep architecture of a graph convolutional network, followed by a supervised feature selection procedure that extracts clusters that are highly predictive of the disease state. By benchmarking GCNCC based on independent datasets from different diseases (psychiatric disorder and cancer) and different platforms (microarray and RNA-seq), we show that GCNCC outperforms other state-of-the-art methods in terms of accuracy and reproducibility.},
doi = {10.1093/bioinformatics/btab772},
journal = {Bioinformatics},
number = 4,
volume = 38,
place = {United States},
year = {Thu Nov 11 00:00:00 EST 2021},
month = {Thu Nov 11 00:00:00 EST 2021}
}
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