ArticleNature communications2022
Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
What it found
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Who cites it
41 citing papers in PubMed, 90 citations in OpenAlex.
- A four‑gene signature identifies TKI‑induced persister cells and uncovers a ZLN005‑induced pyroptotic vulnerability via the GSDME pathway in EGFR‑mutant lung cancer.Molecular medicine reports · 2026Article
- Chemogenomic maps reveal a PRDX1-dependent iron-damage axis in the DNA damage response.Nature chemical biology · 2026Article
- Bimodality in pan-cancer proteomics reveals new opportunities for biomarker discovery.Genome biology · 2026Article
- Why health recommender systems struggle to reach clinical practice: A lifecycle-oriented systematic review.iScience · 2026Article
- Comprehensive molecular characterization of cfDNA as predictive and monitoring biomarkers in advanced gastric cancer receiving immunotherapy.Journal for immunotherapy of cancer · 2026Article
- Advances in understanding the mechanisms underlying acquired resistance to third-generation tyrosine kinase inhibitors in non-small cell lung cancer.Frontiers in cell and developmental biology · 2026Review
- Artemis: Harnessing Knowledge Graphs for Next-Generation Drug Target Prioritization.Computational and structural biotechnology journal · 2026Article
- The STAT3-ZEB1 axis contributes to CCL2-mediated resistance to osimertinib in lung cancer.Frontiers in oncology · 2026Article
- Artificial Intelligence in Computer-Aided Drug Design (CADD) Tools for the Finding of Potent Biologically Active Small Molecules: Traditional to Modern Approach.Combinatorial chemistry & high throughput screening · 2026Review
- Immunotherapy drug target identification using machine learning and patient-derived tumour explant validation.Nature machine intelligence · 2026Article
- Reference-guided computational framework identifies microenvironment metabolic subtypes and targets using pan-cancer single-cell datasets.Genome medicine · 2025Article
- A New Era of Artificial Intelligence (AI): Transforming Drug Discovery and Development.Journal of medicinal chemistry · 2025Article
- TKI resistance related genes in LUAD predict patient prognosis and tumor immune microenvironment with LHX2 as a hub gene.Discover oncology · 2025Article
- Integrative multi-omics identifies AP-1 transcription factor as a targetable mediator of acquired osimertinib resistance in non-small cell lung cancer.Cell death & disease · 2025Article
- Exploiting YES1-Driven EGFR Expression Improves the Efficacy of EGFR Inhibitors.Molecular cancer research : MCR · 2025Article
- Combinatorial CRISPR screen revealseLife · 2025Article
- A benchmark comparison of CRISPRn guide-RNA design algorithms and generation of small single and dual-targeting libraries to boost screening efficiency.BMC genomics · 2025Article
- A novel sensitizer reduces EGFR-TKI resistance by regulating the PI3K/Akt/mTOR pathway and autophagy.Heliyon · 2025Article
- Comprehensive fragmentation of cell-free repetitive DNA for enhanced cancer detection in plasma.Frontiers in cell and developmental biology · 2025Article
- Artificial intelligence in drug development.Nature medicine · 2025Review
Corrections and comments
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Authors and funding
15 authors at 3 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Resistance to EGFR inhibitors (EGFRi) presents a major obstacle in treating non-small cell lung cancer (NSCLC). One of the most exciting new ways to find potential resistance markers involves running functional genetic screens, such as CRISPR, followed by manual triage of significantly enriched genes. This triage process to identify 'high value' hits resulting from the CRISPR screen involves manual curation that requires specialized knowledge and can take even experts several months to comprehensively complete. To find key drivers of resistance faster we build a recommendation system on top of a heterogeneous biomedical knowledge graph integrating pre-clinical, clinical, and literature evidence. The recommender system ranks genes based on trade-offs between diverse types of evidence linking them to potential mechanisms of EGFRi resistance. This unbiased approach identifies 57 resistance markers from >3,000 genes, reducing hit identification time from months to minutes. In addition to reproducing known resistance markers, our method identifies previously unexplored resistance mechanisms that we prospectively validate.
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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.