ArticleBriefings in bioinformatics2024
IBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability.
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 13 papers.
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13 citing papers in PubMed, 14 citations in OpenAlex.
- The Reactome Knowledgebase 2026.Nucleic acids research · 2026Article
- Intra- and inter-multi-omics interaction analysis using deep learning.Bioinformatics advances · 2026Article
- MULGONET: An interpretable neural network framework to integrate multi-omics data for cancer recurrence prediction and biomarker discovery.Fundamental research · 2026Article
- [PSMD11 overexpression promotes epithelial-mesenchymal transition in gastric cancer and affects patient prognosis].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- PTMFusionNet: A Deep Learning Approach for Predicting Disease Related Post-translational Modification and Classifying Disease Subtypes.Molecular & cellular proteomics : MCP · 2025Article
- Effectiveness of Artificial Intelligence Models in Predicting Lung Cancer Recurrence: A Gene Biomarker-Driven Review.Cancers · 2025Review
- GNNMutation: a heterogeneous graph-based framework for cancer detection.BMC bioinformatics · 2025Article
- A review of enhanced biosignature immunotherapy tools for predicting lung cancer immune phenotypes using deep learning.Discover oncology · 2025Review
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- Multimodal data integration in early-stage breast cancer.Breast (Edinburgh, Scotland) · 2025Review
- MethPriorGCN: a deep learning tool for inferring DNA methylation prior knowledge and guiding personalized medicine.Briefings in bioinformatics · 2025Article
- Pathway-guided architectures for interpretable AI in biological research.Computational and structural biotechnology journal · 2025Review
- DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery.Briefings in bioinformatics · 2024Article
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6 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Lung adenocarcinoma (LUAD) is the most common histologic subtype of lung cancer. Early-stage patients have a 30-50% probability of metastatic recurrence after surgical treatment. Here, we propose a new computational framework, Interpretable Biological Pathway Graph Neural Networks (IBPGNET), based on pathway hierarchy relationships to predict LUAD recurrence and explore the internal regulatory mechanisms of LUAD. IBPGNET can integrate different omics data efficiently and provide global interpretability. In addition, our experimental results show that IBPGNET outperforms other classification methods in 5-fold cross-validation. IBPGNET identified PSMC1 and PSMD11 as genes associated with LUAD recurrence, and their expression levels were significantly higher in LUAD cells than in normal cells. The knockdown of PSMC1 and PSMD11 in LUAD cells increased their sensitivity to afatinib and decreased cell migration, invasion and proliferation. In addition, the cells showed significantly lower EGFR expression, indicating that PSMC1 and PSMD11 may mediate therapeutic sensitivity through EGFR expression.
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