ArticleNational science review2025
A deep learning framework for
Article in National science review, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Dissecting oral premalignant carcinogenesis: spatial omics mechanisms and nanomedicine-driven therapeutic innovation.Molecular cancer · 2026Review
- Antibody-drug conjugates in cancer treatment: from molecular design to clinical implementation.The Lancet regional health. Europe · 2026Review
- Traditional Chinese Medicine Modernization in Diagnosis and Treatment: Utilizing Artificial Intelligence and Nanotechnology.MedComm · 2026Review
- scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning.Communications biology · 2026Article
- Multimodal pre-training models of molecular representation for drug discovery.National science review · 2026Review
- Remodeling the Tumor Microenvironment: Mechanistic Insights and Translational Frontiers of TCM-Derived Bioactive Monomers.Cancer management and research · 2026Review
- IntegratingFrontiers in immunology · 2026Review
- The applications of single-cell multiomics in drug screening.Pharmaceutical science advances · 2025Review
- Drug resistance in cancer: molecular mechanisms and emerging treatment strategies.Molecular biomedicine · 2025Review
- Artificial intelligence in pancreatitis: A narrative review on advancing precision diagnosis, prognosis, and therapeutic strategies.World journal of gastroenterology · 2025Review
- Dissecting cross-lineage tumourigenesis under p53 inactivation through single-cell multi-omics and spatial transcriptomics.Clinical and translational medicine · 2025Article
- Multi-omics and single-cell approaches reveal molecular subtypes and key cell interactions in hepatocellular carcinoma.Frontiers in pharmacology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tumor heterogeneity plays a pivotal role in tumor progression and resistance to clinical treatment. Single-cell RNA sequencing (scRNA-seq) enables us to explore heterogeneity within a cell population and identify rare cell types, thereby improving our design of targeted therapeutic strategies. Here, we use a pan-cancer and pan-tissue single-cell transcriptional landscape to reveal heterogeneous expression patterns within malignant cells, precancerous cells, as well as cancer-associated stromal and endothelial cells. We introduce a deep learning framework named Shennong for
Indexed as
Identifiers
What Socratic 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 Socratic graph.