ReviewFundamental research2026
Harnessing multi-omics and machine learning for predicting immune checkpoint blockade responses: Advances, challenges, and future directions.
Review in Fundamental research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis 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
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The application of artificial intelligence in systemic lupus erythematosus: a bibliometric analysis of current trends and future directions.Frontiers in medicine · 2026Pooled it
- TSTScope Unifies Single-Cell Multi-Omics to Identify Functional T Cell States Predictive of Immunotherapy Response.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The mechano-immunological landscape in the tumor microenvironment: From mechanical sensing to a new therapeutic paradigm.Materials today. Bio · 2026Review
- Validity and applicability of machine learning models for systemic lupus erythematosus diagnosis.Lupus science & medicine · 2026Article
- Microbiome dysbiosis and its modulation in cancer development, prevention and therapy.Frontiers in oncology · 2026Review
- AI for biology: Catalyzing interdisciplinary innovation to unravel life's complexity and address biomedical challenges.Fundamental research · 2026Article
- Causal AI for cancer immunotherapy: a narrative framework review of target trial emulation, treatment-effect learning and clinical translation.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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immune checkpoint blockade (ICB) therapies have revolutionized cancer treatment, showing success across various cancer types. However, there is variability in response rates among different cancers and individual patients. This highlights the critical need for precise patient stratification. Machine Learning and Deep Learning models are increasingly utilized to predict ICB responses by integrating multi-omics data, such as clinical, genomic, radiomic, and transcriptomic information. This review outlines the key methodologies of these predictive models. It underscores their role in enhancing response prediction. We delve into the advanced mechanisms of ICB response and discuss the biological foundations that inform these models. This demonstrates how basic research informs clinical application. We aim to offer comprehensive insights into how artificial intelligence can optimize patient stratification for ICB therapy.
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.