ReviewNPJ digital medicine2024
Informing immunotherapy with multi-omics driven machine learning.
Review in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 4 of them syntheses 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
64 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Efficacy and Safety of EGF/EGFR Vaccines in EGFR-Driven Solid Tumors: A Systematic Review and Meta-Analysis of Controlled and Single-Arm Studies.Cancer medicine · 2025Pooled it
- Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy.Frontiers in immunology · 2025Pooled it
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Artificial intelligence in vaccine research and development: an umbrella review.Frontiers in immunology · 2025Pooled it
- Leveraging Microphysiological Systems to Facilitate Neutrophil-Based Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- Immune niches in brain tumors: glial-immune cell interactions and spatial microdomains.Acta neuropathologica communications · 2026Review
- Review
- Closing the Translational Gap: Closed-Loop AI Discovery Frameworks for Experimental Validation and Clinical Implementation in Cancer Therapeutics.Cancer medicine · 2026Review
- Preclinical and Virtual Models of Mucosal Melanoma: Bridging Translational Gaps in a Rare and Lethal Cancer.Pigment cell & melanoma research · 2026Review
- Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Targeting cytokine/chemokine signaling to convert immunologically cold tumors into hot: Emerging strategies in cancer immunotherapy.Acta pharmaceutica Sinica. B · 2026Review
- Artificial Intelligence for Spatial Immunometabolic Analysis of the Tumor Microenvironment: Current Evidence and Future Directions.Current issues in molecular biology · 2026Review
- The neuroimmune axis in breast cancer: from mechanistic insights to clinical applications.BMC medicine · 2026Review
- The evolving role of OMICS in gastrointestinal tumor biology and clinical practice.Molecular cancer · 2026Review
- Decoding immunotherapy response through computational modeling.Nature communications · 2026Review
- Clinically interpretable nomogram combining body composition and clinicopathological features for one year survival prediction in advanced solid tumors.Scientific reports · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Advanced immunotherapy across diseases and the role of artificial intelligence: A review.Biomolecules & biomedicine · 2026Review
- B-cell epitope prediction in the age of machine learning: advancements and challenges.Journal of translational medicine · 2026Review
4 more citing papers are in PubMed but not listed here.
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
Abstract
Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.
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.