ArticleNature methods2022
BIONIC: biological network integration using convolutions.
Article in Nature methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
39 citing papers in PubMed, 58 citations in OpenAlex.
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- Revisiting co-expression-based automated function prediction in yeast with neural networks and updated Gene Ontology annotations.PloS one · 2026Article
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- SubCell: Proteome-aware vision foundation models for microscopy capture single-cell biology.bioRxiv : the preprint server for biology · 2025Article
- DTI-RME: a robust and multi-kernel ensemble approach for drug-target interaction prediction.BMC biology · 2025Article
- A global genetic interaction map of a human cell reveals conserved principles of genetic networks.bioRxiv : the preprint server for biology · 2025Article
- Multiomic Landscape of Extracellular Vesicles in Human Carotid Atherosclerotic Plaque Reveals Endothelial Communication Networks.Arteriosclerosis, thrombosis, and vascular biology · 2025Article
- Who controls the tariffs of a human cell?Molecular systems biology · 2025Article
- Machine learning approaches enable the discovery of therapeutics across domains.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- Integrative network analysis reveals novel moderators of Aβ-Tau interaction in Alzheimer's disease.Alzheimer's research & therapy · 2025Article
- Unifying proteomic technologies with ProteinProjector.Bioinformatics advances · 2025Article
- BADDADAN: Mechanistic modelling of time-series gene module expression.Quantitative plant biology · 2025Article
- ICoN: integration using co-attention across biological networks.Bioinformatics advances · 2025Article
- Heterogeneous network approaches to protein pathway prediction.Computational and structural biotechnology journal · 2024Review
- Drug-target interaction prediction by integrating heterogeneous information with mutual attention network.BMC bioinformatics · 2024Article
- Putting proteins in context.Cell systems · 2024Article
- Progress and opportunities of foundation models in bioinformatics.Briefings in bioinformatics · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 5 institutions in 2 countries.
Funding
Abstract
Biological networks constructed from varied data can be used to map cellular function, but each data type has limitations. Network integration promises to address these limitations by combining and automatically weighting input information to obtain a more accurate and comprehensive representation of the underlying biology. We developed a deep learning-based network integration algorithm that incorporates a graph convolutional network framework. Our method, BIONIC (Biological Network Integration using Convolutions), learns features that contain substantially more functional information compared to existing approaches. BIONIC has unsupervised and semisupervised learning modes, making use of available gene function annotations. BIONIC is scalable in both size and quantity of the input networks, making it feasible to integrate numerous networks on the scale of the human genome. To demonstrate the use of BIONIC in identifying new biology, we predicted and experimentally validated essential gene chemical-genetic interactions from nonessential gene profiles in yeast.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.