ArticleCommunications biology2025
Deciphering cancer therapy resistance via patient-level single-cell transcriptomics with CellResDB.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The trial behind it
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Who cites it
8 citing papers in PubMed.
- Integrated Transcriptomic Analyses Identify Four Prognosis-Associated Genes in Hepatocellular Carcinoma.International journal of molecular sciences · 2026Article
- ICIsAtlas reveals a suppressive NK cell niche in pan-cancer immunotherapy profiles.Communications biology · 2026Article
- Rethinking bioinformatics in liquid-liquid phase separation: data resources, predictive models, and an event-centric perspective.Briefings in bioinformatics · 2026Review
- HybridGNN: a graph neural network approach for human miRNA-disease association prediction.Bioinformatics (Oxford, England) · 2026Article
- cncFinder: A graph-attention-network-based interpretable learning model to identify bifunctional long non-coding RNAs.Molecular therapy. Nucleic acids · 2026Article
- Interferon-primed immune landscapes predict immune-related adverse events during immune checkpoint inhibitor therapy.Frontiers in cell and developmental biology · 2026Article
- Deciphering cancer therapy resistance via patient-level single-cell transcriptomics with CellResDB.Communications biology · 2025Article
- Machine Learning-based Diagnostic Potential of Bipolar Disorder Using Gut Microbiota Signatures.IET systems biologyArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Cancer therapy resistance remains a major challenge, with limited resources available for systematically studying its underlying mechanisms at the patient level. The existing databases are either restricted to bulk RNA-seq data, lack single-cell resolution, or provide limited clinical annotations, making them insufficient for in-depth exploration of the tumor microenvironment (TME) dynamics in therapy resistance. To bridge this gap, we present CellResDB, a patient-derived platform comprising nearly 4.7 million cells from 1391 patient samples across 24 cancer types. CellResDB provides comprehensive annotations of TME features linked to therapy resistance. To enhance accessibility, we include an intelligent robot, CellResDB-Robot, which facilitates intuitive data retrieval and analysis. In summary, CellResDB represents a valuable resource for cancer therapy and provides an experimental protocol for applying large language models (LLMs) within the biomedical database. CellResDB is freely available at https://cellknowledge.com.cn/cellresponse .
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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.