ArticleNPJ digital medicine2021
DeePaN: deep patient graph convolutional network integrating clinico-genomic evidence to stratify lung cancers for immunotherapy.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- Smart-responsive electrospun scaffolds (SRES) for neural repair: Recent advances and future prospects.Bioactive materials · 2026Review
- Semi-Supervised Fatty Liver Classification Using Attention-Based Graph Neural Network Models.Journal of Korean medical science · 2026Article
- The structure-preserving spectral graph neural network for dual kinase inhibitors and synergy scoring in gastric cancer.NPJ digital medicine · 2025Article
- Attention-based multimodal fusion transformer for predicting the efficacy of neoadjuvant therapy in breast cancer: a cross-institutional retrospective study.Breast cancer research : BCR · 2025Article
- Cracking PRMT5: Mechanistic insights, clinical advances, and AI-driven strategies.Cancer letters · 2025Review
- Artificial intelligence for radiopharmaceutical and molecular imaging.Acta pharmaceutica Sinica. B · 2025Review
- HONeYBEE: enabling scalable multimodal AI in oncology through foundation model-driven embeddings.NPJ digital medicine · 2025Article
- scVGAMF: a novel imputation method for scRNA-seq data by integrating linear and non-linear features.Briefings in bioinformatics · 2025Article
- A review of enhanced biosignature immunotherapy tools for predicting lung cancer immune phenotypes using deep learning.Discover oncology · 2025Review
- Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development.Clinical and translational science · 2024Review
- Ten challenges and opportunities in computational immuno-oncology.Journal for immunotherapy of cancer · 2024Review
- Increasing psychopharmacology clinical trial success rates with digital measures and biomarkers: Future methods.NPP - digital psychiatry and neuroscience · 2024Review
- TrialView: An AI-powered Visual Analytics System for Temporal Event Data in Clinical Trials.Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences · 2024Article
- Deep learning facilitates multi-data type analysis and predictive biomarker discovery in cancer precision medicine.Computational and structural biotechnology journal · 2023Review
- The CD40 agonist HERA-CD40L results in enhanced activation of antigen presenting cells, promoting an anti-tumor effect alone and in combination with radiotherapy.Frontiers in immunology · 2023Article
- Deep-Learning Algorithm and Concomitant Biomarker Identification for NSCLC Prediction Using Multi-Omics Data Integration.Biomolecules · 2022Article
- Heterogeneous graph construction and HinSAGE learning from electronic medical records.Scientific reports · 2022Article
- Predicting miRNA-disease associations via layer attention graph convolutional network model.BMC medical informatics and decision making · 2022Article
- Article
- Artificial Intelligence for Precision Oncology.Advances in experimental medicine and biology · 2022Review
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
Abstract
Immuno-oncology (IO) therapies have transformed the therapeutic landscape of non-small cell lung cancer (NSCLC). However, patient responses to IO are variable and influenced by a heterogeneous combination of health, immune, and tumor factors. There is a pressing need to discover the distinct NSCLC subgroups that influence response. We have developed a deep patient graph convolutional network, we call "DeePaN", to discover NSCLC complexity across data modalities impacting IO benefit. DeePaN employs high-dimensional data derived from both real-world evidence (RWE)-based electronic health records (EHRs) and genomics across 1937 IO-treated NSCLC patients. DeePaN demonstrated effectiveness to stratify patients into subgroups with significantly different (P-value of 2.2 × 10
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.