ReviewGut2025
Artificial intelligence applied to 'omics data in liver disease: towards a personalised approach for diagnosis, prognosis and treatment.
Review in Gut, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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Who cites it
60 citing papers in PubMed.
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- Nutriomic Technologies for Characterizing, Diagnosing, Clustering and Managing Chronic Liver Diseases: Precision Nutrition Implications.International journal of molecular sciences · 2026Review
- Identification of Pre-Diagnostic Protein Biomarkers for Liver Cirrhosis Based on Prospective Analysis of a Large-Scale Plasma Proteomics in the UK Biobank.Proteomics. Clinical applications · 2026Article
- Review
- Integrating omics and artificial intelligence in pediatric environmental health: tools, challenges, and cohort-based insights.Pediatric research · 2026Review
- Cross-Model Explainability Consistency in Hepatitis C Stage Classification: A SHAP, LIME, and Counterfactual Analysis Across Five Machine Learning Architectures.Diagnostics (Basel, Switzerland) · 2026Article
- Spatial hepatology: Decoding liver zonation for metabolic and regenerative therapeutics (Review).International journal of molecular medicine · 2026Review
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- Machine learning-driven evaluation of protein kinase D3 as a co-diagnostic biomarker in hepatocellular carcinoma.Journal of Zhejiang University. Science. B · 2026Article
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Bioinformatics and experimental validation of druggable targets in non-alcoholic fatty liver disease.Scientific reports · 2026Article
- Article
- A Non-Invasive Integrated Model for Accurate Preoperative Identification of the Aggressive Macrotrabecular-Massive Subtype of Hepatocellular Carcinoma: A Single-Center Retrospective Study.Diagnostics (Basel, Switzerland) · 2026Article
- [Advances in basic and experimental diagnostic research on liver diseases in 2025].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026Article
- MASLD biomarker discovery: evaluating lipidomics techniques across disease progression.Molecular biology reports · 2026Review
- Applications of Artificial Intelligence and Smart Devices in Metabolic Dysfunction-associated Steatotic Liver Disease.Journal of clinical and translational hepatology · 2026Article
- Integrating multi-omics and machine learning systematically deciphers cellular heterogeneity and fibrotic regulatory networks in the progression from MASLD to MASH.NPJ digital medicine · 2026Article
- Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches.World journal of gastroenterology · 2026Review
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- Machine learning-based identification of targeted metabolomic biomarkers for early diagnosis and fibrosis-stage discrimination in metabolic dysfunction-associated steatotic liver disease.Frontiers in nutrition · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advancements in omics technologies and artificial intelligence (AI) methodologies are fuelling our progress towards personalised diagnosis, prognosis and treatment strategies in hepatology. This review provides a comprehensive overview of the current landscape of AI methods used for analysis of omics data in liver diseases. We present an overview of the prevalence of different omics levels across various liver diseases, as well as categorise the AI methodology used across the studies. Specifically, we highlight the predominance of transcriptomic and genomic profiling and the relatively sparse exploration of other levels such as the proteome and methylome, which represent untapped potential for novel insights. Publicly available database initiatives such as The Cancer Genome Atlas and The International Cancer Genome Consortium have paved the way for advancements in the diagnosis and treatment of hepatocellular carcinoma. However, the same availability of large omics datasets remains limited for other liver diseases. Furthermore, the application of sophisticated AI methods to handle the complexities of multiomics datasets requires substantial data to train and validate the models and faces challenges in achieving bias-free results with clinical utility. Strategies to address the paucity of data and capitalise on opportunities are discussed. Given the substantial global burden of chronic liver diseases, it is imperative that multicentre collaborations be established to generate large-scale omics data for early disease recognition and intervention. Exploring advanced AI methods is also necessary to maximise the potential of these datasets and improve early detection and personalised treatment strategies.
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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.