ReviewBioData mining2025
Network-based multi-omics integrative analysis methods in drug discovery: a systematic review.
Review in BioData mining, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.
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
45 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- High-flow nasal cannula for acute respiratory failure: a bibliometric analysis of current trends and future directions.Frontiers in medicine · 2026Pooled it
- Global research trends in tuberculosis drug-resistance mechanisms: a bibliometric and cross-database analysis.Frontiers in microbiology · 2026Pooled it
- Multi-omics graph attention network reveals neuro-immune-tumor pathways in breast cancer liver depression syndrome.Frontiers in immunology · 2026Trial
- Artificial intelligence-driven discovery of coumarin-based therapeutics: Revolutionizing target identification and validation.Pharmaceutical science advances · 2026Review
- Gut‑liver‑kidney axis: A systems biology framework for understanding and treating chronic kidney disease (Review).International journal of molecular medicine · 2026Review
- Review
- Insights into the Complexities of Pharmacotherapy Parameters in Artificial Intelligence Models for Drug Selection, Precision Personalised Medicine and Optimal Therapeutic Outcomes.Pharmaceutics · 2026Review
- Integrated transcriptomic and proteomic profiling reveals alteration of oxidative phosphorylation in omega-3 polyunsaturated fatty acid-mediated inhibition of Candida albicans biofilm formation.BMC microbiology · 2026Article
- Targeting MAPK Pathways in Skin, Thyroid, and Pancreatic Cancer: A Perspective on Synthetic Inhibitors and Natural Modulators.Advanced biology · 2026Review
- Improving recombinant protein productivity in CHO cells via multi-omics data integration.Bioresources and bioprocessing · 2026Review
- Diet and Lipidomics Mediated Regulation of Mesenchymal Stem Cell Function: Diet, Omics and Stem Cell Connection.Biomolecules · 2026Review
- Mechanistic Artificial Intelligence for Personalized Drug Therapy: Integrating Pharmacokinetics, Pharmacodynamics, Therapeutic Drug Monitoring, and Multiomic Systems Biology.Pharmaceutics · 2026Review
- Mathematical and Computational Models of Biochemical Reactions and Cell Signaling-From Ordinary Differential Equations to Machine Learning.International journal of molecular sciences · 2026Review
- Integrating omics and artificial intelligence in pediatric environmental health: tools, challenges, and cohort-based insights.Pediatric research · 2026Review
- ProteinNetworkSight: a user-friendly platform for transforming co-expression patterns into actionable therapeutic insights through interactive network visualization.Nucleic acids research · 2026Article
- Medicinal Chemistry Perspectives on Drug Repurposing: Innovative Approaches and Therapeutic Breakthrough.Therapeutic innovation & regulatory science · 2026Review
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Molecular maps of diseases from omics data and network embeddings.NPJ systems biology and applications · 2026Article
- Review
- A Scalable Sign-Aware Multi-Omics Knowledge Graph Foundation Model for Mechanistic Drug Action and Clinical Response Predictions.bioRxiv : the preprint server for biology · 2026Article
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
The integration of multi-omics data from diverse high-throughput technologies has revolutionized drug discovery. While various network-based methods have been developed to integrate multi-omics data, systematic evaluation and comparison of these methods remain challenging. This review aims to analyze network-based approaches for multi-omics integration and evaluate their applications in drug discovery. We conducted a comprehensive review of literature (2015-2024) on network-based multi-omics integration methods in drug discovery, and categorized methods into four primary types: network propagation/diffusion, similarity-based approaches, graph neural networks, and network inference models. We also discussed the applications of the methods in three scenario of drug discovery, including drug target identification, drug response prediction, and drug repurposing, and finally evaluated the performance of the methods by highlighting their advantages and limitations in specific applications. While network-based multi-omics integration has shown promise in drug discovery, challenges remain in computational scalability, data integration, and biological interpretation. Future developments should focus on incorporating temporal and spatial dynamics, improving model interpretability, and establishing standardized evaluation frameworks.
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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.