ReviewFrontiers in immunology2024
From multi-omics to predictive biomarker: AI in tumor microenvironment.
Review in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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.
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
14 citing papers in PubMed.
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Article
- The evolving role of OMICS in gastrointestinal tumor biology and clinical practice.Molecular cancer · 2026Review
- Artificial intelligence-powered liquid biopsy in cancer: a paradigm shift in cancer detection and personalized care.Cancer cell international · 2026Review
- Artificial intelligence-assisted spatial omics-based biomimetic nanoplatform for intelligent and precise intervention in the immunosuppressive core region of ovarian cancer.NPJ precision oncology · 2026Review
- Clonal evolution in gastrointestinal cancers: multi-omics insights into tumor heterogeneity, microenvironmental selection, and translational biomarkers.Frontiers in oncology · 2026Review
- Parasitic Infections and Carcinogenesis: Molecular Mechanisms, Immune Modulation, and Emerging Therapeutic Strategies.Oncology research · 2026Review
- How do immunometabolites shape bacterial infections?PLoS biology · 2026Article
- The gut microbiota-aromatic amino acid axis in cardiovascular disease: pathophysiological roles, translational biomarkers, and therapeutic targeting.Frontiers in cardiovascular medicine · 2026Review
- Therapeutic vulnerability shaped by the microenvironment: multi-omics and AI biomarkers for precision surgical planning in gastrointestinal tumors.Frontiers in cell and developmental biology · 2026Article
- Review
- Innovative Approaches to EMT-Related Biomarker Identification in Breast Cancer: Multi-Omics and Machine Learning Methods.Biotech (Basel (Switzerland)) · 2025Review
- Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions.Biomedicines · 2025Review
- AI-Powered Insights into Drug Resistance in Gastric Cancer: A Path Toward Precision Therapy.Iranian journal of pharmaceutical research : IJPRReview
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
In recent years, tumors have emerged as a major global health threat. An increasing number of studies indicate that the production, development, metastasis, and elimination of tumor cells are closely related to the tumor microenvironment (TME). Advances in artificial intelligence (AI) algorithms, particularly in large language models, have rapidly propelled research in the medical field. This review focuses on the current state and strategies of applying AI algorithms to tumor metabolism studies and explores expression differences between tumor cells and normal cells. The analysis is conducted from the perspectives of metabolomics and interactions within the TME, further examining the roles of various cytokines. This review describes the potential approaches through which AI algorithms can facilitate tumor metabolic studies, which offers a valuable perspective for a deeper understanding of the pathological mechanisms of tumors.
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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.