ArticleBriefings in bioinformatics2024
D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed, 6 citations in OpenAlex.
- Applications of artificial intelligence in non-small cell lung cancer: from precision diagnosis to personalized prognosis and therapy.Journal of translational medicine · 2025Review
- Deep learning enhances precision diagnosis and treatment of non-small cell lung cancer: future prospects.Translational lung cancer research · 2025Review
- Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.Molecular cancer · 2025Review
- Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer.Briefings in bioinformatics · 2024Review
- Epidemiological and clinical characteristics of lung cancer in Saudi Arabia: a retrospective study in single oncology center.Oncology research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As key oncogenic drivers in non-small-cell lung cancer (NSCLC), various mutations in the epidermal growth factor receptor (EGFR) with variable drug sensitivities have been a major obstacle for precision medicine. To achieve clinical-level drug recommendations, a platform for clinical patient case retrieval and reliable drug sensitivity prediction is highly expected. Therefore, we built a database, D3EGFRdb, with the clinicopathologic characteristics and drug responses of 1339 patients with EGFR mutations via literature mining. On the basis of D3EGFRdb, we developed a deep learning-based prediction model, D3EGFRAI, for drug sensitivity prediction of new EGFR mutation-driven NSCLC. Model validations of D3EGFRAI showed a prediction accuracy of 0.81 and 0.85 for patients from D3EGFRdb and our hospitals, respectively. Furthermore, mutation scanning of the crucial residues inside drug-binding pockets, which may occur in the future, was performed to explore their drug sensitivity changes. D3EGFR is the first platform to achieve clinical-level drug response prediction of all approved small molecule drugs for EGFR mutation-driven lung cancer and is freely accessible at https://www.d3pharma.com/D3EGFR/index.php.
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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.