Deep graph representations embed network information for robust disease marker identification

Maddouri, Omar; Qian, Xiaoning; Yoon, Byung-Jun

doi:10.1093/bioinformatics/btab772

Deep graph representations embed network information for robust disease marker identification

Journal Article · Thu Nov 11 04:00:00 EST 2021 · Bioinformatics

DOI:https://doi.org/10.1093/bioinformatics/btab772· OSTI ID:1855099

^[1]; Qian, Xiaoning ^[2]; ^[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)

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.

View Accepted Manuscript (DOE)

Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0012704

OSTI ID:: 1855099

Report Number(s):: BNL-222809-2022-JAAM

Journal Information:: Bioinformatics, Journal Name: Bioinformatics Journal Issue: 4 Vol. 38; ISSN 1367-4803

Publisher:: International Society for Computational Biology - Oxford University PressCopyright Statement

Country of Publication:: United States

Language:: English

References (31)

Genome-wide expression analysis reveals dyregulation of myelination-related genes in chronic schizophrenia Hakak, Y.; Walker, J. R.; Li, C. European Psychiatry, Vol. 17 https://doi.org/10.1016/S0924-9338(02)80108-0	journal	May 2002
Techniques for clustering gene expression data Kerr, G.; Ruskin, H. J.; Crane, M. Computers in Biology and Medicine, Vol. 38, Issue 3 https://doi.org/10.1016/j.compbiomed.2007.11.001	journal	March 2008
Network‐based classification of breast cancer metastasis Chuang, Han‐Yu; Lee, Eunjung; Liu, Yu‐Tsueng Molecular Systems Biology, Vol. 3, Issue 1 https://doi.org/10.1038/msb4100180	journal	January 2007
Dynamic regulatory network controlling TH17 cell differentiation Yosef, Nir; Shalek, Alex K.; Gaublomme, Jellert T. Nature, Vol. 496, Issue 7446 https://doi.org/10.1038/nature11981	journal	March 2013
A molecular signature of metastasis in primary solid tumors Ramaswamy, Sridhar; Ross, Ken N.; Lander, Eric S. Nature Genetics, Vol. 33, Issue 1 https://doi.org/10.1038/ng1060	journal	December 2002
Identification of transcriptional regulators in the mouse immune system Jojic, Vladimir; Shay, Tal; Sylvia, Katelyn Nature Immunology, Vol. 14, Issue 6 https://doi.org/10.1038/ni.2587	journal	April 2013
Wisdom of crowds for robust gene network inference Marbach, Daniel; Costello, James C.; Küffner, Robert Nature Methods, Vol. 9, Issue 8 https://doi.org/10.1038/nmeth.2016	journal	July 2012
Network-based stratification of tumor mutations Hofree, Matan; Shen, John P.; Carter, Hannah Nature Methods, Vol. 10, Issue 11 https://doi.org/10.1038/nmeth.2651	journal	September 2013
Advantages and limitations of current network inference methods De Smet, Riet; Marchal, Kathleen Nature Reviews Microbiology, Vol. 8, Issue 10 https://doi.org/10.1038/nrmicro2419	journal	August 2010
A comprehensive evaluation of module detection methods for gene expression data Saelens, Wouter; Cannoodt, Robrecht; Saeys, Yvan Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-03424-4	journal	March 2018
Modularity and community structure in networks Newman, M. E. J. Proceedings of the National Academy of Sciences, Vol. 103, Issue 23 https://doi.org/10.1073/pnas.0601602103	journal	May 2006
A pathway-based classification of human breast cancer Gatza, M. L.; Lucas, J. E.; Barry, W. T. Proceedings of the National Academy of Sciences, Vol. 107, Issue 15 https://doi.org/10.1073/pnas.0912708107	journal	March 2010
Tissue Classification with Gene Expression Profiles Ben-Dor, Amir; Bruhn, Laurakay; Friedman, Nir Journal of Computational Biology, Vol. 7, Issue 3-4 https://doi.org/10.1089/106652700750050943	journal	August 2000
A comparative analysis of biclustering algorithms for gene expression data Eren, K.; Deveci, M.; Kucuktunc, O. Briefings in Bioinformatics, Vol. 14, Issue 3 https://doi.org/10.1093/bib/bbs032	journal	July 2012
Genesis: cluster analysis of microarray data Sturn, A.; Quackenbush, J.; Trajanoski, Z. Bioinformatics, Vol. 18, Issue 1 https://doi.org/10.1093/bioinformatics/18.1.207	journal	January 2002
STRING v10: protein–protein interaction networks, integrated over the tree of life Szklarczyk, Damian; Franceschini, Andrea; Wyder, Stefan Nucleic Acids Research, Vol. 43, Issue D1 https://doi.org/10.1093/nar/gku1003	journal	October 2014
Enrichr: a comprehensive gene set enrichment analysis web server 2016 update Kuleshov, Maxim V.; Jones, Matthew R.; Rouillard, Andrew D. Nucleic Acids Research, Vol. 44, Issue W1 https://doi.org/10.1093/nar/gkw377	journal	May 2016
DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants Piñero, Janet; Bravo, Àlex; Queralt-Rosinach, Núria Nucleic Acids Research, Vol. 45, Issue D1 https://doi.org/10.1093/nar/gkw943	journal	October 2016
Clustering by Passing Messages Between Data Points Frey, B. J.; Dueck, D. Science, Vol. 315, Issue 5814 https://doi.org/10.1126/science.1136800	journal	February 2007
Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring Golub, T. R. Science, Vol. 286, Issue 5439, p. 531-537 https://doi.org/10.1126/science.286.5439.531	journal	October 1999
Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap Gandal, Michael J.; Haney, Jillian R.; Parikshak, Neelroop N. Science, Vol. 359, Issue 6376 https://doi.org/10.1126/science.aad6469	journal	February 2018
Autism as a Paradigmatic Complex Genetic Disorder Veenstra-VanderWeele, Jeremy; Christian, Susan L.; Cook, Jr., Edwin H. Annual Review of Genomics and Human Genetics, Vol. 5, Issue 1 https://doi.org/10.1146/annurev.genom.5.061903.180050	journal	September 2004
Identification of diagnostic subnetwork markers for cancer in human protein-protein interaction network Su, Junjie; Yoon, Byung-Jun; Dougherty, Edward R. BMC Bioinformatics, Vol. 11, Issue S6 https://doi.org/10.1186/1471-2105-11-S6-S8	journal	October 2010
Incorporating topological information for predicting robust cancer subnetwork markers in human protein-protein interaction network Khunlertgit, Navadon; Yoon, Byung-Jun BMC Bioinformatics, Vol. 17, Issue S13 https://doi.org/10.1186/s12859-016-1224-1	journal	October 2016
Inferring Pathway Activity toward Precise Disease Classification Lee, Eunjung; Chuang, Han-Yu; Kim, Jong-Won PLoS Computational Biology, Vol. 4, Issue 11 https://doi.org/10.1371/journal.pcbi.1000217	journal	November 2008
Chapter 5: Network Biology Approach to Complex Diseases Cho, Dong-Yeon; Kim, Yoo-Ah; Przytycka, Teresa M. PLoS Computational Biology, Vol. 8, Issue 12 https://doi.org/10.1371/journal.pcbi.1002820	journal	December 2012
Integrated Module and Gene-Specific Regulatory Inference Implicates Upstream Signaling Networks Roy, Sushmita; Lagree, Stephen; Hou, Zhonggang PLoS Computational Biology, Vol. 9, Issue 10 https://doi.org/10.1371/journal.pcbi.1003252	journal	October 2013
Integrating Genome-Wide Genetic Variations and Monocyte Expression Data Reveals Trans-Regulated Gene Modules in Humans Rotival, Maxime; Zeller, Tanja; Wild, Philipp S. PLoS Genetics, Vol. 7, Issue 12 https://doi.org/10.1371/journal.pgen.1002367	journal	December 2011
Accurate and Reliable Cancer Classification Based on Probabilistic Inference of Pathway Activity Su, Junjie; Yoon, Byung-Jun; Dougherty, Edward R. PLoS ONE, Vol. 4, Issue 12 https://doi.org/10.1371/journal.pone.0008161	journal	December 2009
A General Framework for Weighted Gene Co-Expression Network Analysis Zhang, Bin; Horvath, Steve Statistical Applications in Genetics and Molecular Biology, Vol. 4, Issue 1 https://doi.org/10.2202/1544-6115.1128	journal	January 2005
Editorial: Comorbidity and Autism Spectrum Disorder Casanova, Manuel F.; Frye, Richard E.; Gillberg, Christopher Frontiers in Psychiatry, Vol. 11 https://doi.org/10.3389/fpsyt.2020.617395	journal	November 2020

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