Systematic Evaluation of Molecular Networks for Discovery of Disease Genes
Open accessAbstract
Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall. A general tendency is that performance scales with network size, suggesting that new interaction discovery currently outweighs the detrimental effects of false positives. Correcting for size, we find that the DIP network provides the highest efficiency (value per interaction). Based on these results, we create a parsimonious composite network with both high efficiency and performance. This work provides a benchmark for selection of molecular networks in human disease research.
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The random walk approach from Huang et al. provides a biologically relevant benchmarking approach to determine a GRN's ability to model disease subnetworks.
“This approach provides a biologically relevant benchmarking approach to determine a GRNs ability to model disease subnetworks.”
From SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics (2023) · cites this paper as [35]
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Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1131 · Cited by 17,749 · see citing works
Szklarczyk, D., Gable, A. L., Nastou, K. C., Lyon, D., Kirsch, R., Pyysalo, S., Doncheva, N. T., Legeay, M., Fang, T., Bork, P., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1074 · Cited by 8,056 · see citing works
Dual proteome-scale networks reveal cell-specific remodeling of the human interactome
Huttlin, E. L., Bruckner, R. J., Navarrete-Perea, J., Cannon, J. R., Baltier, K., Gebreab, F., Gygi, M. P., Thornock, A., Zarraga, G., Tam, S., et al. (2020) · doi:10.1101/2020.01.19.905109 · Cited by 960 · see citing works
Target identification among known drugs by deep learning from heterogeneous networks
Zeng, X., Zhu, S., Lu, W., Liu, Z., Huang, J., Zhou, Y., Fang, J., Huang, Y., Guo, H., Li, L., et al. (2020) · Chemical Science · doi:10.1039/c9sc04336e · Cited by 357 · see citing works
Self-citationBig data in IBD: big progress for clinical practice
Seyed Tabib, N. S., Madgwick, M., Sudhakar, P., Verstockt, B., Korcsmaros, T., & Vermeire, S. (2020) · Gut · doi:10.1136/gutjnl-2019-320065 · Cited by 223 · see citing works
HumanNet v2: human gene networks for disease research
Hwang, S., Kim, C. Y., Yang, S., Kim, E., Hart, T., Marcotte, E. M., & Lee, I. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1126 · Cited by 210 · see citing works
Network analysis reveals rare disease signatures across multiple levels of biological organization
Buphamalai, P., Kokotovic, T., Nagy, V., & Menche, J. (2021) · Nature Communications · doi:10.1038/s41467-021-26674-1 · Cited by 95 · see citing works
Reconstruction of Cell-type-Specific Interactomes at Single-Cell Resolution
Mohammadi, S., Davila-Velderrain, J., & Kellis, M. (2019) · Cell Systems · doi:10.1016/j.cels.2019.10.007 · Cited by 73 · see citing works
Sudhakar, P., Verstockt, B., Cremer, J., Verstockt, S., Sabino, J., Ferrante, M., & Vermeire, S. (2021) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaa281 · Cited by 49 · see citing works
A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases
Zolotareva, O., & Kleine, M. (2019) · Journal of Integrative Bioinformatics · doi:10.1515/jib-2018-0069 · Cited by 46 · see citing works
Littman, R., Cheng, M., Wang, N., Peng, C., & Yang, X. (2023) · iScience · doi:10.1016/j.isci.2023.107124 · Cited by 35 · see citing works
Lin, S., & Jia, P. (2024) · GigaScience · doi:10.1093/gigascience/giae108 · Cited by 4 · see citing works
Analysis of intracellular and intercellular crosstalk from omics data
Chiodi, A., Pelucchi, P., & Mosca, E. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.10.552776
- Authors
- Huang, J. K., Carlin, D. E., Yu, M. K., Zhang, W., Kreisberg, J. F., Tamayo, P., & Ideker, T.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Huang, Justin K.
- ORCID
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 6
- Issue
- 4
- Pages
- 484-495.e5
- ISSN
- 2405-4712 (ISSN-L)
- PMC
- PMC5920724
- OpenAlex
- W2794479004
- Cited by
- 289
- References
- 91
- Citations by year
- 2026: 23 · 2025: 35 · 2024: 35 · 2023: 32 · 2022: 26 · 2021: 60 · 2020: 35 · 2019: 32 · 2018: 11
- FWCI
- 12.27
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell Systems (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Defense Advanced Research Projects Agency (W911-NF-14-1-0397)
- National Institutes of Health (R01 HG009979, P30 CA023100, U54 CA209891, T32 GM008806, R01 CA154480, R01 CA121941, P41 GM103504, U24 CA184427, OT3 TR002026, P50 GM085764)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- disease gene discovery, gene interaction networks, protein-protein interaction network, network benchmarking, network selection, network size, false positives, composite networks, strings, giants, DIP, human disease genes
- MeSH terms
- Algorithms, Humans, Genome, Human, Computational Biology, Genetic Predisposition to Disease, Gene Regulatory Networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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