ReviewNature reviews. Cancer2026
Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.
Review in Nature reviews. Cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Organoid Intelligent Morphomics: Decoding the organoid morphome through artificial intelligence from phenotypic quantification to mechanistic insight.Bioactive materials · 2027Review
- Natural product therapy in diabetic kidney disease: emerging multiomics-mediated signalling pathway and molecular target.Chinese medicine · 2026Review
- Digital Twins for Targeted Therapy in Head and Neck Cancer: From Molecular Stratification to Resistance-Aware Combination Strategies.Current oncology (Toronto, Ont.) · 2026Review
- The Gut-Immune-Brain Axis in Aging: Integrating Immunosenescence, Inflammaging, and Neuroinflammation for Precision Medicine.Medical sciences (Basel, Switzerland) · 2026Review
- Article
- Mapping the path to clinical implementation of multi-omics.Nature genetics · 2026Review
- Navigating AI and machine learning in cancer research: an end-to-end translational framework.Journal of translational medicine · 2026Review
- Article
- Review
- Crosstalk between innate immune signaling pathways and integrated TLR, NLRP3 inflammasome, cGAS-STING, and NF-κB networks in sepsis.Frontiers in cell and developmental biology · 2026Review
- Unravelling the nexus of non-coding RNAs in cancer stemness and therapeutic drug resistance.Frontiers in cell and developmental biology · 2026Review
- Editorial: Artificial intelligence in multi-omics: advancing tumor metastasis prediction and mechanism analysis.Frontiers in cell and developmental biology · 2026Article
- Immunotherapy rechallenge in gastric cancer: resistance mechanisms, molecular stratification, and precision decision-making.Frontiers in immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The extensive heterogeneity of cancer across biological scales necessitates a holistic approach beyond single-analyte methods. Integrating multi-omics data - from genomics to proteomics - with multimodal information, such as clinical records and medical imaging, offers a comprehensive, systems-level view of tumorigenesis. Artificial intelligence (AI) has emerged as the essential technology to decipher these complex, high-dimensional datasets, powering substantial advances in early diagnosis, precise patient stratification, prediction of therapeutic response and the elucidation of mechanisms of drug resistance. To translate these powerful predictive models into practice, explainable AI is critical for building clinical trust and generating novel, testable biological hypotheses. While challenges in data accessibility and model generalizability persist, the field is advancing toward patient-specific digital twins, promising to simulate individual disease trajectories and optimize treatments, thereby heralding a new era of precision oncology.
Indexed as
Identifiers
42014628What Socratic 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 Socratic graph.