ArticleFrontiers in genetics2021
Graph Neural Networks and Their Current Applications in Bioinformatics.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 135 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
135 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The role of AI-assisted drug repurposing in neurological disorders: a systematic review of validation strategies, challenges and opportunities.Journal of nanobiotechnology · 2026Pooled it
- Graph neural networks for single-cell omics data: a review of approaches and applications.Briefings in bioinformatics · 2025Pooled it
- Next-generation Janus kinase inhibitors: Integrating synthetic innovation, structural biology, and computational design for precision drug discovery.Pharmaceutical science advances · 2026Review
- Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Artificial intelligence-driven advancements in agricultural biotechnology.Journal, genetic engineering & biotechnology · 2026Review
- engGNN: a dual-graph neural network for omics-based disease classification and feature selection.Briefings in bioinformatics · 2026Article
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- Graph in Graph (GiG): A novel graph AI framework for integrating and interpreting medical and omics data.bioRxiv : the preprint server for biology · 2026Article
- [Application of artificial intelligence for surgical skill assessment and quality control in minimally invasive surgery: progress, problems and prospect].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Review
- The molecular ICU: a primer on omics, informatics and the future of precision critical care.Critical care (London, England) · 2026Review
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- A survey of models composed of graph neural networks and large language models for molecular science.Bioinformatics (Oxford, England) · 2026Review
- LeGenD: High-throughput N-glycan profiling using explainable AI and lectin profiling.The Journal of biological chemistry · 2026Article
- The evolution of AI-integrated genome editing and its challenges.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Review
- Network methods for diagonal integration of unpaired single-cell multiomics data: a review.Bioinformatics (Oxford, England) · 2026Review
- TF-DWGNet: a directed weighted graph neural network with tensor fusion for multi-omics cancer subtype classification.NAR genomics and bioinformatics · 2026Article
- AI and Machine Learning for Proteomics-Driven Drug Discovery: Methods, Tools, and Best Practices.Current issues in molecular biology · 2026Review
- A Bimodal Graph Neural Network with Transfer Learning and Contrastive Learning for Protein-Protein Interaction Site Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- Review of Progress of AI in Biomimetics: From Biological Patterns to Closed-Loop Discovery.Biomimetics (Basel, Switzerland) · 2026Review
75 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics. In this research, a systematic survey of GNNs and their advances in bioinformatics is presented from multiple perspectives. We first introduce some commonly used GNN models and their basic principles. Then, three representative tasks are proposed based on the three levels of structural information that can be learned by GNNs: node classification, link prediction, and graph generation. Meanwhile, according to the specific applications for various omics data, we categorize and discuss the related studies in three aspects: disease prediction, drug discovery, and biomedical imaging. Based on the analysis, we provide an outlook on the shortcomings of current studies and point out their developing prospect. Although GNNs have achieved excellent results in many biological tasks at present, they still face challenges in terms of low-quality data processing, methodology, and interpretability and have a long road ahead. We believe that GNNs are potentially an excellent method that solves various biological problems in bioinformatics research.
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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.