ArticleiScience2022
Deep neural network modeling identifies biomarkers of response to immune-checkpoint therapy.
Article in iScience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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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.
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Who cites it
22 citing papers in PubMed.
- Transcriptional states define dependencies and therapeutic vulnerabilities in head and neck cancer.NPJ precision oncology · 2026Article
- Profiling the tumor immunological status in Japanese solid cancers using the tumor immune status scoring algorithm.Scientific reports · 2026Article
- ICIsc: A Deep Learning Framework for Predicting Immune Checkpoint Inhibitor Response by Integrating scRNA-Seq and Protein Language Models.Bioengineering (Basel, Switzerland) · 2026Article
- Advanced immunotherapy across diseases and the role of artificial intelligence: A review.Biomolecules & biomedicine · 2026Review
- Unpacking Genomic Biomarkers for Programmed Cell Death Receptor-1 Immunotherapy Success in Non-Small Cell Lung Cancer Using Deep Neural Networks: Quantitative Study.JMIR bioinformatics and biotechnology · 2026Article
- Annotation-free prediction of immunotherapy response in melanoma using single-cell transcriptomic data.PloS one · 2026Article
- Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer.Journal of inflammation research · 2026Review
- Machine learning approaches for biomarker discovery using single-cell RNA sequencing.Frontiers in bioinformatics · 2026Review
- Subtype-Specific Dependencies and Drug Vulnerabilities Enable Precision Therapeutics in Head and Neck Cancer.bioRxiv : the preprint server for biology · 2025Article
- cytoGPNet: Enhancing clinical outcome prediction accuracy using longitudinal cytometry data in small cohort studies.Patterns (New York, N.Y.) · 2025Article
- Machine learning-guided single-cell multiomics uncovers GDF15-driven immunosuppressive niches in NSCLC: A translational framework for overcoming anti-PD-1 resistance.Translational oncology · 2025Article
- Evaluating the factors influencing accuracy, interpretability, and reproducibility in the use of machine learning classifiers in biology to enable standardization.Scientific reports · 2025Article
- PathNetDRP: a novel biomarker discovery framework using pathway and protein-protein interaction networks for immune checkpoint inhibitor response prediction.BMC bioinformatics · 2025Article
- Uncovering gene and cellular signatures of immune checkpoint response via machine learning and single-cell RNA-seq.NPJ precision oncology · 2025Article
- A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer.Cancers · 2025Review
- Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Artificial Intelligence in Metabolomics: A Current Review.Trends in analytical chemistry : TRAC · 2024Article
- TME-NET: an interpretable deep neural network for predicting pan-cancer immune checkpoint inhibitor responses.Briefings in bioinformatics · 2024Article
- BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks.Nature communications · 2024Article
- Informing immunotherapy with multi-omics driven machine learning.NPJ digital medicine · 2024Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Immunotherapy has shown significant promise as a treatment for cancer, such as lung cancer and melanoma. However, only 10%-30% of the patients respond to treatment with immune checkpoint blockers (ICBs), underscoring the need for biomarkers to predict response to immunotherapy. Here, we developed DeepGeneX, a computational framework that uses advanced deep neural network modeling and feature elimination to reduce single-cell RNA-seq data on ∼26,000 genes to six of the most important genes (
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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.