ReviewBriefings in bioinformatics2021
Biological network analysis with deep learning.
Review in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
93 citing papers in PubMed.
- Multimodal Artificial Intelligence in Lung Cancer: From Data Integration to Precision Oncology.Cancers · 2026Review
- IdentifyingInfluential Nodes in Complex Networks Based on the Integration of Smallest-Cycle and Non-Smallest-Cycle Features.Entropy (Basel, Switzerland) · 2026Article
- From Marine Peptides to Oncology: The Therapeutic Potential of Conotoxins in Cancer Treatment.Chemistry & biodiversity · 2026Review
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- AI-Guided DNA-Free and Genotype-Independent Genome Editing for Soybean Improvement.Plants (Basel, Switzerland) · 2026Review
- The evolution of AI-integrated genome editing and its challenges.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Review
- Hypergraph representations of single-cell RNA sequencing data for improved cell clustering.Bioinformatics (Oxford, England) · 2026Article
- StabLyzeGraph: High-throughput screening of combinatorial mutations using graph neural networks.Protein science : a publication of the Protein Society · 2026Article
- AI-Driven Drug Discovery: Focus on Targets for Solid Tumors.Pharmaceutics · 2026Review
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- Enhanced drug disease association prediction through multimodal data integration and meta path guided global local feature fusion.Scientific reports · 2026Article
- Network Analysis to Identify MicroRNAs Involved in Alzheimer's Disease and to Improve Drug Prioritization.Biomedicines · 2026Article
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- Integrating Drought Stress Signaling and Smart Breeding for Climate-Resilient Crops: Regulatory Mechanisms and Genetic Strategies.Plants (Basel, Switzerland) · 2025Review
- Deep Learning-Based Prediction of Individual CellBiosensors · 2025Article
- GraphComm predicts cell cell communication using a graph based deep learning method in single cell RNA sequencing data.Scientific reports · 2025Article
- An Introduction to Network Analysis in Plant Biology.Plant direct · 2025Article
- Broadening dementia risk models: building on the 2024 Lancet Commission report for a more inclusive global framework.EBioMedicine · 2025Review
- Moving From Point-Based Analysis to Systems-Based Modeling: Knowledge Integration to Address Antimicrobial Resistance.CPT: pharmacometrics & systems pharmacology · 2025Article
- Estimating personal temporal symptom networks for childhood cancer survivors.Communications medicine · 2025Article
33 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in the form of a biological network. The rise of this data has created a need for new computational tools to analyze networks. One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). In this article, we describe biological networks and review the principles and underlying algorithms of GNNs. We then discuss domains in bioinformatics in which graph neural networks are frequently being applied at the moment, such as protein function prediction, protein-protein interaction prediction and in silico drug discovery and development. Finally, we highlight application areas such as gene regulatory networks and disease diagnosis where deep learning is emerging as a new tool to answer classic questions like gene interaction prediction and automatic disease prediction from data.
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What OpenQuestion holds
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.