ReviewGigaScience2025
Emerging AI approaches for cancer spatial omics.
Review in GigaScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Deep Learning-Assisted Prioritization of Candidate Drug Targets in Tumors Using Spatial Multi-Omics.Biology · 2026Review
- Spatially organized regulated cell death-immune coupling in solid tumors: integrating spatial omics with actionable regulated cell death biology.Molecular cancer · 2026Review
- Diet-Microbiota-Immune Interactions in Hepatocellular Carcinoma: An Immunometabolic and Spatial Perspective.Nutrients · 2026Review
- Toward a Conceptual Multiscale Framework for Predictive Radiobiology: Integrating Genomic Damage, Network Rewiring, and Tissue Microenvironment.International journal of molecular sciences · 2026Review
- The mechano-immunological landscape in the tumor microenvironment: From mechanical sensing to a new therapeutic paradigm.Materials today. Bio · 2026Review
- Review
- Integrating multi-omics data for next-generation cancer research and precision medicine.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Digital Pathology and the AI-Based Quantification of the Tumor Microenvironment in Gastrointestinal Cancer: From Tumor Budding and Tumor-Infiltrating Lymphocytes to Tertiary Lymphoid Structures.International journal of molecular sciences · 2026Review
- Navigating cell maps by deep learning integration of single-cell and spatially resolved transcriptomics.Briefings in bioinformatics · 2026Article
- Next-Generation Artificial Intelligence Strategies for Mechanistic Cancer Target Discovery and Drug Development: A State-of-the-Art Review.International journal of molecular sciences · 2026Review
- From spatial maps to therapeutic targets: Next challenge for artificial intelligence in cancer spatial omics.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Multi-Scale Transcriptomics Redefining the Tumor Immune Microenvironment.Biotech (Basel (Switzerland)) · 2026Review
- The spatial revolution in immuno-oncology: artificial intelligence decoding NK cell niches to predict therapeutic response.Frontiers in immunology · 2026Review
- AI-driven insights into protein misfolding and innate immunity in neurodegenerative diseases.Frontiers in immunology · 2026Review
- Artificial Intelligence-Enabled Multi-Omics for Predicting Immune Checkpoint Inhibitor Response and Resistance.Journal of multidisciplinary healthcare · 2026Review
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- Artificial intelligence in ovarian cancer: advancing in precision diagnosis and clinical management.Frontiers in immunology · 2026Review
Corrections and comments
- Update of
Authors and funding
3 authors.
Funding
Abstract
Technological breakthroughs in spatial omics and artificial intelligence (AI) have the potential to transform the understanding of cancer cells and the tumor microenvironment. Here we review the role of AI in spatial omics, discussing the current state-of-the-art and further needs to decipher cancer biology from large-scale spatial tissue data. An overarching challenge is the development of interpretable spatial AI models, an activity that demands not only improved data integration but also new conceptual frameworks. We discuss emerging paradigms-in particular, data-driven spatial AI, constraint-based spatial AI, and mechanistic spatial modeling-as well as the importance of integrating AI with hypothesis-driven strategies and model systems to realize the value of cancer spatial information.
Indexed as
Identifiers
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.