A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks
Open accessAbstract
Background: Molecular interaction networks summarize complex biological processes as graphs, whose structure is informative of biological function at multiple scales. Simultaneously, omics technologies measure the variation or activity of genes, proteins, or metabolites across individuals or experimental conditions. Integrating the complementary viewpoints of biological networks and omics data is an important task in bioinformatics, but existing methods treat networks as discrete structures, which are intrinsically difficult to integrate with continuous node features or activity measures. Graph neural networks map graph nodes into a low-dimensional vector space representation, and can be trained to preserve both the local graph structure and the similarity between node features. Results: We studied the representation of transcriptional, protein–protein and genetic interaction networks in E. coli and mouse using graph neural networks. We found that such representations explain a large proportion of variation in gene expression data, and that using gene expression data as node features improves the reconstruction of the graph from the embedding. We further proposed a new end-to-end Graph Feature Auto-Encoder framework for the prediction of node features utilizing the structure of the gene networks, which is trained on the feature prediction task, and showed that it performs better at predicting unobserved node features than regular MultiLayer Perceptrons. When applied to the problem of imputing missing data in single-cell RNAseq data, the Graph Feature Auto-Encoder utilizing our new graph convolution layer called FeatGraphConv outperformed a state-of-the-art imputation method that does not use protein interaction information, showing the benefit of integrating biological networks and omics data with our proposed approach. Conclusion: Our proposed Graph Feature Auto-Encoder framework is a powerful approach for integrating and exploiting the close relation between molecular interaction networks and functional genomics data.
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Gene Ontology terms are a standardized vocabulary for molecular functions, biological processes, and cellular locations of gene products.
“Gene Ontology (GO) terms [125] are a standardized vocabulary for describing molecular functions, biological processes, and cellular locations of gene products [126].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [125]
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Cites
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853 · see citing works
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000 · see citing works
Inductive Representation Learning on Large Graphs
Hamilton, W. L., Ying, R., & Leskovec, J. (2017) · arXiv · doi:10.48550/arxiv.1706.02216 · Cited by 10,639 · see citing works
Network biology: understanding the cell's functional organization
Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903 · see citing works
Neural Message Passing for Quantum ChemistryPreprint
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01212 · Cited by 5,414 · see citing works
Erdős, P., & Rényi, A. (2022) · Publicationes Mathematicae Debrecen · doi:10.5486/pmd.1959.6.3-4.12 · Cited by 5,337 · see citing works
Variational Graph Auto-EncodersPreprint
Kipf, T., & Welling, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.07308 · Cited by 3,635 · see citing works
A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles
Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., et al. (2017) · Cell · doi:10.1016/j.cell.2017.10.049 · Cited by 3,515 · see citing works
Fast Graph Representation Learning with PyTorch GeometricPreprint
Fey, M., & Lenssen, J. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.02428 · Cited by 3,403 · see citing works
Multi-omics approaches to disease
Hasin, Y., Seldin, M., & Lusis, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1215-1 · Cited by 2,953 · see citing works
An Introduction to Systems Biology: Design Principles of Biological Circuits
Alon, U. (2006) · doi:10.1201/9781420011432 · Cited by 2,825 · see citing works
Representation Learning on Graphs: Methods and ApplicationsPreprint
Hamilton, W. L., Ying, R., & Leskovec, J. (2017) · IEEE Data Engineering Bulletin · doi:10.48550/arxiv.1709.05584 · Cited by 2,203 · see citing works
Wavelets on graphs via spectral graph theory
Hammond, D. K., Vandergheynst, P., & Gribonval, R. (2010) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2010.04.005 · Cited by 2,173 · see citing works
Recovering Gene Interactions from Single-Cell Data Using Data Diffusion
van Dijk, D., Sharma, R., Nainys, J., Yim, K., Kathail, P., Carr, A. J., Burdziak, C., Moon, K. R., Chaffer, C. L., Pattabiraman, D., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.061 · Cited by 1,829 · see citing works
The BioGRID interaction database: 2019 update
Oughtred, R., Stark, C., Breitkreutz, B., Rust, J., Boucher, L., Chang, C., Kolas, N. K., O’Donnell, L., Leung, G. C., McAdam, R. F., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky1079 · Cited by 1,440 · see citing works
Methods of integrating data to uncover genotype-phenotype interactions
Ritchie, M. D., Holzinger, E. R., Li, R., Pendergrass, S. A., & Kim, D. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3868 · Cited by 1,085 · see citing works
Network propagation: a universal amplifier of genetic associations
Cowen, L., Ideker, T. E., Raphael, B. J., & Sharan, R. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.38 · Cited by 666 · see citing works
Getting connected: analysis and principles of biological networksPreprint
Zhu, X., Gerstein, M., & Snyder, M. P. (2007) · Genes & Development · doi:10.1101/gad.1528707 · Cited by 549 · see citing works
Genetics meets proteomics: perspectives for large population-based studies
Suhre, K., McCarthy, M. I., & Schwenk, J. M. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-020-0268-2 · Cited by 478 · see citing works
Faith, J. J., Driscoll, M. E., Fusaro, V. A., Cosgrove, E. J., Hayete, B., Juhn, F., Schneider, S. J., & Gardner, T. S. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm815 · Cited by 259 · see citing works
Machine Learning on Graphs: A Model and Comprehensive TaxonomyPreprint
Chami, I., Abu-El-Haija, S., Perozzi, B., Ré, C., & Murphy, K. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.03675 · Cited by 228 · see citing works
Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks
Morris, C., Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., & Grohe, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014602 · Cited by 172 · see citing works
Cheng, S., Pei, Y., He, L., Peng, G., Reinius, B., Tam, P., Jing, N., & Deng, Q. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.02.031 · Cited by 141 · see citing works
netSmooth: Network-smoothing based imputation for single cell RNA-seq
Ronen, J., & Akalin, A. (2018) · F1000Research · doi:10.12688/f1000research.13511.3 · Cited by 59 · see citing works
A Comprehensive Survey of Tools and Software for Active Subnetwork Identification
Nguyen, H., Shrestha, S., Tran, D., Shafi, A., Drăghici, S., & Nguyen, T. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00155 · Cited by 57 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [25]Faith JJ Driscoll ME Fusaro VA Cosgrove EJ Hayete B Juhn FS Schneider SJ Gardner TS Many microbe microarrays database: uniformly normalized affymetrix compendia with structured experimental metadata Nucleic Acids Res 2008 36 Database-Issue 866 870 PMC2238822 17932051
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Li, M. M., Huang, K., & Zitnik, M. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00942-x · Cited by 294 · see citing works
“Additionally, gene-interaction networks that leverage transcriptomic data 105,125 can capture context-specific interactions between genes (Fig.”
Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review
Lee, M. (2023) · Molecules · doi:10.3390/molecules28135169 · Cited by 93 · see citing works
DiNuzzo, M. (2022) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2022.1019706 · Cited by 16 · see citing works
Li, M. M., Huang, K., & Žitnik, M. (2021) · arXiv (Cornell University) · Cited by 14 · see citing works
Graph embedding and geometric deep learning relevance to network biology and structural chemistry
Lecca, P., & Lecca, M. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1256352 · Cited by 13 · see citing works
Chen, Y.-H., Shih, Y.-T., Chien, C. ‐., & Tsai, C.-S. (2022) · PLoS ONE · doi:10.1371/journal.pone.0266435 · Cited by 12 · see citing works
Cinaglia, P. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05830-6 · Cited by 11 · see citing works
Ferebee, T. H., & Buckler, E. S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.11.540406 · Cited by 7 · see citing works
Malla, A. M., & Banka, A. A. (2025) · Expert Systems · doi:10.1111/exsy.70091 · Cited by 8 · see citing works
A Survey of GNN in Bioinformation DataPreprint
Zhu, Z. (2022) · Preprints.org · doi:10.20944/preprints202209.0226.v1 · Cited by 4 · see citing works
Graph Edits for Counterfactual Explanations: A Comparative Study
Dimitriou, A., Chaidos, N., Lymperaiou, M., & Stamou, G. B. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63797-1_6 · Cited by 2 · see citing works
CrossNet: A Hybrid Neural Network Model for Cross-Domain e-Commerce Shopping Behavior Prediction
Ni, W., & Chen, Q. (2025) · Journal of Circuits Systems and Computers · doi:10.1142/s021812662550361x · Cited by 2 · see citing works
Gong, J., Zhao, Y., Heng, X., Chen, Y., Sun, P., He, F., Ma, Z., & Ren, Z. (2023) · Electronic Research Archive · doi:10.3934/era.2023253 · Cited by 2 · see citing works
Yao, W., & li, H. (2026) · Alexandria Engineering Journal · doi:10.1016/j.aej.2026.03.003 · Cited by 1 · see citing works
Network Embedding for Peripheral Weak Genetic Effects of Complex Diseases
Li, J., Wang, B., Han, C., & He, J. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822247
Yadalam, P. K., Krithikadatta, J., Natarajan, P. M., & Ardila, C. M. (2025) · Scientific Reports · doi:10.1038/s41598-025-12074-8
Sc-GRIP: a Graph Convolutional Approach to Infer Gene Interaction Polarity from Single-Cell Data
Atria, C. E., Cai, Y., Weber, P., Beer, A., Kriege, N. M., Böhm, C., Revilla‐i‐Domingo, R., & Plant, C. (2025) · Proceedings · doi:10.1109/icdm65498.2025.00111
Hybrid intelligence for breast cancer biomarker discovery
Rakhshaninejad, M., Fathian, M., Yazdanjue, N., & Gandomi, A. H. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-28940-8.00021-x
Zhou, R., & Zhang, Y. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08961-0
- Authors
- Hasibi, R., & Michoel, T.
- Affiliations
- University of Bergen (NO)
- Corresponding author
- Hasibi, Ramin
- Venue
- BMC Bioinformatics
- Published
- 2021-10-27
- Volume
- 22
- Issue
- 1
- Pages
- 525-525
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC8554915
- OpenAlex
- W3117334649
- Cited by
- 19
- References
- 24
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 3 · 2023: 4 · 2022: 4 · 2021: 1
- FWCI
- 0.94
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Figshare (cc-by)
- Funders
- Norges Forskningsråd (312045)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- molecular interaction networks, protein-protein interaction network, genetic interaction networks, gene expression, graph convolution, graph neural networks, Escherichia coli, mice
- MeSH terms
- Animals, Escherichia coli, Proteins, Neural Networks, Computer, Computational Biology, Mice, Gene Regulatory Networks
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