Disease Module Identification Based on Representation Learning of Complex Networks Integrated From GWAS, eQTL Summaries, and Human Interactome
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The study of disease-relevant gene modules is one of the main methods to discover disease pathway and potential drug targets. Recent studies have found that most disease proteins tend to form many separate connected components and scatter across the protein-protein interaction network. However, most of the research on discovering disease modules are biased toward well-studied seed genes, which tend to extend seed genes into a single connected subnetwork. In this paper, we propose N2V-HC, an algorithm framework aiming to unbiasedly discover the scattered disease modules based on deep representation learning of integrated multi-layer biological networks. Our method first predicts disease associated genes based on summary data of Genome-wide Association Studies (GWAS) and expression Quantitative Trait Loci (eQTL) studies, and generates an integrated network on the basis of human interactome. The features of nodes in the network are then extracted by deep representation learning. Hierarchical clustering with dynamic tree cut methods are applied to discover the modules that are enriched with disease associated genes. The evaluation on real networks and simulated networks show that N2V-HC performs better than existing methods in network module discovery. Case studies on Parkinson's disease and Alzheimer's disease, show that N2V-HC can be used to discover biological meaningful modules related to the pathways underlying complex diseases.
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Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625 · see citing works
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247 · see citing works
Self-citationNormalized cuts and image segmentation
Shi, J., & Malik, J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.868688 · Cited by 15,842 · see citing works
Community structure in social and biological networks
Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502 · see citing works
The Molecular Signatures Database Hallmark Gene Set Collection
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015) · Cell Systems · doi:10.1016/j.cels.2015.12.004 · Cited by 13,807 · see citing works
node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926 · see citing works
Mootha, V. K., Lindgren, C. M., Eriksson, K.-F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstråle, M., Laurila, E., et al. (2003) · Nature Genetics · doi:10.1038/ng1180 · Cited by 10,197 · see citing works
DeepWalk: online learning of social representations
Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623732 · Cited by 10,123 · see citing works
Clustering by Passing Messages Between Data Points
Frey, B. J., & Dueck, D. (2007) · Science · doi:10.1126/science.1136800 · Cited by 6,733 · see citing works
An automated method for finding molecular complexes in large protein interaction networks
Bader, G. D., & Hogue, C. W. V. (2003) · BMC Bioinformatics · doi:10.1186/1471-2105-4-2 · Cited by 6,112 · see citing works
An Information Flow Model for Conflict and Fission in Small Groups
Zachary, W. W. (1977) · Journal of Anthropological Research · doi:10.1086/jar.33.4.3629752 · Cited by 4,709 · see citing works
Functional mapping and annotation of genetic associations with FUMA
Watanabe, K., Taskesen, E., van Bochoven, A., & Posthuma, D. (2017) · Nature Communications · doi:10.1038/s41467-017-01261-5 · Cited by 4,083 · see citing works
Genetic effects on gene expression across human tissues
GTEx Consortium (2017) · Nature · doi:10.1038/nature24277 · Cited by 4,036 · see citing works
10 Years of GWAS Discovery: Biology, Function, and Translation
Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.06.005 · Cited by 3,719 · see citing works
An efficient algorithm for large-scale detection of protein families
Enright, A. J. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.7.1575 · Cited by 3,536 · see citing works
Benchmark graphs for testing community detection algorithms
Lancichinetti, A., Fortunato, S., & Radicchi, F. (2008) · Physical Review E · doi:10.1103/physreve.78.046110 · Cited by 3,058 · see citing works
Nalls, M. A., Blauwendraat, C., Vallerga, C. L., Heilbron, K., Bandres-Ciga, S., Chang, D., Tan, M., Kia, D. A., Noyce, A. J., Xue, A., et al. (2019) · The Lancet Neurology · doi:10.1016/s1474-4422(19)30320-5 · Cited by 2,454 · see citing works
Lusseau, D., Schneider, K., Boisseau, O. J., Haase, P. A., Slooten, E., & Dawson, S. (2003) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-003-0651-y · Cited by 2,283 · see citing works
Jansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., Sealock, J., Karlsson, I. K., Hägg, S., Athanasiu, L., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0311-9 · Cited by 2,277 · see citing works
Self-citationDefining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R
Langfelder, P., Zhang, B., & Horvath, S. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btm563 · Cited by 1,892 · see citing works
Systematic identification of trans eQTLs as putative drivers of known disease associations
Westra, H.-J., Peters, M. J., Esko, T., Yaghootkar, H., Schurmann, C., Kettunen, J., Christiansen, M. W., Fairfax, B. P., Schramm, K., Powell, J. E., et al. (2013) · Nature Genetics · doi:10.1038/ng.2756 · Cited by 1,658 · see citing works
Uncovering disease-disease relationships through the incomplete interactome
Menche, J., Sharma, A., Kitsak, M., Ghiassian, S. D., Vidal, M., Loscalzo, J., & Barabási, A.-L. (2015) · Science · doi:10.1126/science.1257601 · Cited by 1,546 · see citing works
Cooperative Game Theory Approaches for Network Partitioning
Avrachenkov, K. E., Kondratev, A. Y., & Mazalov, V. V. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-62389-4_49 · Cited by 1,075 · see citing works
The Post-GWAS Era: From Association to Function
Gallagher, M. D., & Chen-Plotkin, A. S. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.04.002 · Cited by 822 · see citing works
Fairfax, B. P., Makino, S., Radhakrishnan, J., Plant, K., Leslie, S., Dilthey, A., Ellis, P., Langford, C., Vannberg, F. O., & Knight, J. C. (2012) · Nature Genetics · doi:10.1038/ng.2205 · Cited by 486 · see citing works
Defects in trafficking bridge Parkinson’s disease pathology and genetics
Abeliovich, A., & Gitler, A. D. (2016) · Nature · doi:10.1038/nature20414 · Cited by 462 · see citing works
Fuzzy communities and the concept of bridgeness in complex networks
Nepusz, T., Petróczi, A., Négyessy, L., & Bazsó, F. (2008) · Physical Review E · doi:10.1103/physreve.77.016107 · Cited by 414 · see citing works
Ghiassian, S. D., Menche, J., & Barabási, A.-Ĺ. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004120 · Cited by 388 · see citing works
Complex I deficiency in Parkinson's disease frontal cortex
Parker, W. D., Parks, J. K., & Swerdlow, R. H. (2007) · Brain Research · doi:10.1016/j.brainres.2007.10.061 · Cited by 383 · see citing works
The role of JAK-STAT signaling within the CNS
Nicolas, C. S., Amici, M., Bortolotto, Z. A., Doherty, A. J., Csaba, Z., Fafouri, A., Dournaud, P., Gressèns, P., Collingridge, G. L., & Peineau, S. (2013) · JAK-STAT · doi:10.4161/jkst.22925 · Cited by 352 · see citing works
Lysosomal impairment in Parkinson's disease
Dehay, B., Martinez‐Vicente, M., Caldwell, G. A., Caldwell, K. A., Yue, Z., Cookson, M. R., Klein, C., Vila, M., & Bezard, E. (2013) · Movement Disorders · doi:10.1002/mds.25462 · Cited by 340 · see citing works
GLay: community structure analysis of biological networks
Su, G., Kuchinsky, A., Morris, J., States, D., & Meng, F. (2010) · Bioinform. · doi:10.1093/bioinformatics/btq596 · Cited by 273 · see citing works
Leveraging models of cell regulation and GWAS data in integrative network-based association studies
Califano, A., Butte, A. J., Friend, S. H., Ideker, T., & Schadt, E. E. (2012) · Nature Genetics · doi:10.1038/ng.2355 · Cited by 257 · see citing works
Inflammation and Adaptive Immunity in Parkinson's DiseasePreprint
Mosley, R. L., Hutter-Saunders, J. A. L., Stone, D. K., & Gendelman, H. E. (2011) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a009381 · Cited by 246 · see citing works
Integrative Genomics Reveals Novel Molecular Pathways and Gene Networks for Coronary Artery Disease
Mäkinen, V.-P., Civelek, M., Meng, Q., Zhang, B., Zhu, J., Levian, C., Huan, T., Segrè, A. V., Ghosh, S., Vivar, J., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004502 · Cited by 196 · see citing works
Cheng, L., Qi, C., Zhuang, H., Fu, T., & Zhang, X. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz843 · Cited by 195 · see citing works
Sharma, A., Menche, J., Huang, C., Ort, T., Zhou, X., Kitsak, M., Sahni, N., Thibault, D. M., Voung, L., Guo, F., et al. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv001 · Cited by 193 · see citing works
A learning-based framework for miRNA-disease association identification using neural networks
Peng, J., Hui, W., Li, Q., Chen, B., Hao, J., Jiang, Q., Shang, X., & Wei, Z. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz254 · Cited by 180 · see citing works
Integrins as receptor targets for neurological disorders
Wu, X., & Reddy, D. S. (2012) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2011.12.008 · Cited by 175 · see citing works
Retinoic acid counteracts developmental defects in the substantia nigra caused by Pitx3 deficiency
Jacobs, F. M. J., Smits, S. M., Noorlander, C. W., von Oerthel, L., van der Linden, A. J. A., Burbach, J. P. H., & Smidt, M. P. (2007) · Development · doi:10.1242/dev.02865 · Cited by 141 · see citing works
Sphingolipids in the Pathogenesis of Parkinson’s Disease and Parkinsonism
Lin, G., Wang, L., Marcogliese, P. C., & Bellen, H. J. (2018) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2018.11.003 · Cited by 127 · see citing works
Cheng, L., Qi, C., Zhuang, H., Fu, T., & Zhang, X. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa511 · Cited by 125 · see citing works
Arenas, E. (2014) · Journal of Molecular Cell Biology · doi:10.1093/jmcb/mju001 · Cited by 122 · see citing works
Computational Methods for Identifying Similar Diseases
Cheng, L., Zhao, H., Wang, P., Zhou, W., Luo, M., Li, T., Han, J., Liu, S., & Jiang, Q. (2019) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2019.09.019 · Cited by 120 · see citing works
Plácido, A. I., Pereira, C., Duarte, A. I., Candeias, E., Correia, S. C., Santos, R. X., Carvalho, C., Cardoso, S. M., Oliveira, C. R., & Moreira, P. I. (2014) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2014.05.003 · Cited by 118 · see citing works
MetSigDis: a manually curated resource for the metabolic signatures of diseases
Cheng, L., Yang, H., Zhao, H., et al. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx103 · Cited by 117 · see citing works
Collective Spammer Detection in Evolving Multi-Relational Social Networks
Fakhraei, S., Foulds, J. R., Shashanka, M. V. S., & Getoor, L. C. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2788606 · Cited by 112 · see citing works
En1 and Wnt signaling in midbrain dopaminergic neuronal development
dos Santos, M. T. A., & Smidt, M. P. (2011) · Neural Development · doi:10.1186/1749-8104-6-23 · Cited by 111 · see citing works
Calabrese, G. M., Mesner, L. D., Stains, J. P., Tommasini, S. M., Horowitz, M. C., Rosen, C. J., & Farber, C. R. (2016) · Cell Systems · doi:10.1016/j.cels.2016.10.014 · Cited by 107 · see citing works
Large-scale analysis of disease pathways in the human interactome
Agrawal, M., Žitnik, M., & Leskovec, J. (2017) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813235533_0011 · Cited by 106 · see citing works
Exploring regulation in tissues with eQTL networks
Fagny, M., Paulson, J. N., Kuijjer, M. L., Sonawane, A. R., Chen, C.-Y., Lopes‐Ramos, C. M., Glass, K., Quackenbush, J. F., & Platig, J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1707375114 · Cited by 103 · see citing works
Wang, S., Zhang, S., Liou, L.-C., Ren, Q., Zhang, Z., Caldwell, G. A., Caldwell, K. A., & Witt, S. N. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1411694111 · Cited by 102 · see citing works
Dong, X., Liao, Z., Gritsch, D., Hadzhiev, Y., Bai, Y., Locascio, J. J., Guennewig, B., Liu, G., Blauwendraat, C., Wang, T., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0223-0 · Cited by 94 · see citing works
Self-citationIntegrating multi-network topology for gene function prediction using deep neural networks
Peng, J., Xue, H., Wei, Z., Tuncali, I., Hao, J., & Shang, X. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa036 · Cited by 94 · see citing works
Class II G Protein-Coupled Receptors and Their Ligands in Neuronal Function and Protection
Martin, B., de Maturana, R. L., Brenneman, R., Walent, T., Mattson, M. P., & Maudsley, S. R. (2005) · NeuroMolecular Medicine · doi:10.1385/nmm:7:1-2:003 · Cited by 89 · see citing works
InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk
Cheng, L., Jiang, Y., Ju, H., Sun, J., Peng, J., Zhou, M., & Hu, Y. (2018) · BMC Genomics · doi:10.1186/s12864-017-4338-6 · Cited by 89 · see citing works
Esteves, M., Cristóvão, A. C., Saraiva, T., Rocha, S. M., Baltazar, G., Ferreira, L., & Bernardino, L. (2015) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2015.00020 · Cited by 88 · see citing works
Cheng, L., Zhuang, H., Yang, S., Jiang, H., Wang, S., & Zhang, J. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00657 · Cited by 82 · see citing works
Innate immune activation in Alzheimer’s disease
Wang, M., Miao, D., Cao, X., Tan, L., & Tan, L. (2018) · Annals of Translational Medicine · doi:10.21037/atm.2018.04.20 · Cited by 74 · see citing works
Mihara, T., Nakashima, M., Kuroiwa, A., Akitake, Y., Ono, K., Hosokawa, M., Yamada, T., & Takahashi, M. (2007) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2007.05.013 · Cited by 66 · see citing works
Potassium Channels: A Potential Therapeutic Target for Parkinson’s Disease
Chen, X., Xue, B., Wang, J., Liu, H., Shi, L., & Xie, J. (2017) · Neuroscience Bulletin · doi:10.1007/s12264-017-0177-3 · Cited by 57 · see citing works
Peng, J., Wang, X., & Shang, X. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2769-6 · Cited by 55 · see citing works
New Roles of Glycosaminoglycans in α-Synuclein Aggregation in a Cellular Model of Parkinson Disease
Lehri-Boufala, S., Ouidja, M. O., Barbier-Chassefière, V., Hénault, E., Raisman‐Vozari, R., Garrigue‐Antar, L., Papy-García, D., & Morin, C. (2015) · PLoS ONE · doi:10.1371/journal.pone.0116641 · Cited by 46 · see citing works
Defining a role for hemoglobin in Parkinson’s disease
Freed, J., & Chakrabarti, L. (2016) · npj Parkinson s Disease · doi:10.1038/npjparkd.2016.21 · Cited by 31 · see citing works
FSM: Fast and scalable network motif discovery for exploring higher-order network organizations
Wang, T., Peng, J., Peng, Q., et al. (2019) · Methods · doi:10.1016/j.ymeth.2019.07.008 · Cited by 27 · see citing works
Self-citationeQTLMAPT: Fast and Accurate eQTL Mediation Analysis With Efficient Permutation Testing Approaches
Wang, T., Peng, Q., Liu, B., Liu, X., Liu, Y., Peng, J., & Wang, Y. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2019.01309 · Cited by 16 · see citing works
Self-citationIdentifying emerging phenomenon in long temporal phenotyping experiments
Peng, J., Lu, J., Hoh, D., Dina, A. S., Shang, X., Kramer, D., & Chen, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz559 · Cited by 7 · see citing works
An automated quality control pipeline for eQTL analysis with RNA-seq data
Wang, T., Ruan, J., Yin, Q., Dong, X., & Wang, Y. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983006 · Cited by 6 · see citing works
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Graph representation learning in biomedicine and healthcare
Li, M. M., Huang, K., & Zitnik, M. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00942-x · Cited by 294 · see citing works
“By re-imagining genome-wide-association studies and expression-quantitative-trait-loci studies 203 as networks, biologically meaningful modules can be discovered that highlight key genes involved in the underlying mechanisms of a disease 204 .”
- Authors
- Wang, T., Peng, Q., Liu, B., Liu, Y., & Wang, Y.
- Affiliations
- Harbin Institute of Technology (CN)
- Corresponding author
- Liu, Bo
- Liu, Yongzhuang
- Wang, Yadong
- Venue
- Frontiers in Bioengineering and Biotechnology
- Publisher
- Frontiers Media SA
- Published
- 2020-05-06
- Volume
- 8
- Pages
- 418-418
- ISSN
- 2296-4185 (ISSN-L)
- PMC
- PMC7218106
- OpenAlex
- W3022603009
- Cited by
- 27
- References
- 69
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 6 · 2022: 9 · 2021: 6
- FWCI
- 1.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Bioengineering and Biotechnology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key Research and Development Program of China (2017YFC0907503, 2017YFC1201201)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- network module detection, deep representation learning, GWAS summary data, human interactome, hierarchical clustering, Parkinson's disease, Alzheimer's disease, complex diseases, drug target identification
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