ArticleDiagnostics (Basel, Switzerland)2023
Cancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial Intelligence in Breast Cancer Research.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 48 citations in OpenAlex.
- FOXM1 Signaling Network Transcriptionally Upregulates Expression of Proteins Involved in Mitotic Progression to Induce High Proliferation and Chromosomal Instability in Androgen Receptor-Low Triple-Negative Breast Cancer.International journal of molecular sciences · 2026Article
- Circulating extrachromosomal circular DNA as a prognostic biomarker for colorectal cancer.Cell communication and signaling : CCS · 2026Article
- Qoppa as a New Pan-Tumor Synthetic Parameter Derived from Tumor-Associated Biomarkers for Identifying Oncology Patients at High Risk of Metastasis: A Prospective Pilot Study.Journal of clinical medicine · 2026Article
- Article
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- Accuracy is not enough: explainable boosting machine model and identification of candidate biomarkers for real-time sepsis risk assessment in the emergency department.BMC emergency medicine · 2025Article
- Routine Laboratory Tests Predict 72-h Fatality in Patients With D-Dimer Levels ≥ 2 μg/mL: A Retrospective Cohort Study Comparing Statistical and Machine Learning Models.Journal of clinical laboratory analysis · 2025Article
- Explainable AI in Clinical Decision Support Systems: A Meta-Analysis of Methods, Applications, and Usability Challenges.Healthcare (Basel, Switzerland) · 2025Review
- Precision Enhanced Bioactivity Prediction of Tyrosine Kinase Inhibitors by Integrating Deep Learning and Molecular Fingerprints Towards Cost-Effective and Targeted Cancer Therapy.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Breast Lesion Detection Using Weakly Dependent Customized Features and Machine Learning Models with Explainable Artificial Intelligence.Journal of imaging · 2025Article
- Identification of biomarkers associated with M1 macrophages in the ST-segment elevation myocardial infarction through bioinformatics and machine learning approaches.Scientific reports · 2025Article
- Machine learning-based risk prediction model for pertussis in children: a multicenter retrospective study.BMC infectious diseases · 2025Article
- Untargeted Lipidomic Biomarkers for Liver Cancer Diagnosis: A Tree-Based Machine Learning Model Enhanced by Explainable Artificial Intelligence.Medicina (Kaunas, Lithuania) · 2025Article
- Integration of CRISPR/dCas9-Based methylation editing with guide positioning sequencing identifies dynamic changes of mrDEGs in breast cancer progression.Cellular and molecular life sciences : CMLS · 2025Article
- Investigating the Key Trends in Applying Artificial Intelligence to Health Technologies: A Scoping Review.PloS one · 2025Article
- Prediction of obesity levels based on physical activity and eating habits with a machine learning model integrated with explainable artificial intelligence.Frontiers in physiology · 2025Article
- Approaches to modeling cancer metastasis: from bench to bedside.Frontiers in oncology · 2025Review
- Integrating convolutional neural networks with ensemble methods for enhanced diabetes diagnosis: a multi-dataset evaluation.Frontiers in medicine · 2025Article
- From ductal carcinoma in situ to invasive breast cancer: the prognostic value of the extracellular microenvironment.Journal of experimental & clinical cancer research : CR · 2024Review
- Association between albumin-corrected anion gap and kidney function in individuals with hypertension - NHANES 2009-2016 cycle.Renal failure · 2024Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimMethod: This research presents a model combining machine learning (ML) techniques and eXplainable artificial intelligence (XAI) to predict breast cancer (BC) metastasis and reveal important genomic biomarkers in metastasis patients.
methodA total of 98 primary BC samples was analyzed, comprising 34 samples from patients who developed distant metastases within a 5-year follow-up period and 44 samples from patients who remained disease-free for at least 5 years after diagnosis. Genomic data were then subjected to biostatistical analysis, followed by the application of the elastic net feature selection method. This technique identified a restricted number of genomic biomarkers associated with BC metastasis. A light gradient boosting machine (LightGBM), categorical boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Gradient Boosting Trees (GBT), and Ada boosting (AdaBoost) algorithms were utilized for prediction. To assess the models' predictive abilities, the accuracy, F1 score, precision, recall, area under the ROC curve (AUC), and Brier score were calculated as performance evaluation metrics. To promote interpretability and overcome the "black box" problem of ML models, a SHapley Additive exPlanations (SHAP) method was employed.
resultsThe LightGBM model outperformed other models, yielding remarkable accuracy of 96% and an AUC of 99.3%. In addition to biostatistical evaluation, in XAI-based SHAP results, increased expression levels of TSPYL5, ATP5E, CA9, NUP210, SLC37A1, ARIH1, PSMD7, UBQLN1, PRAME, and UBE2T (
conclusionThe findings of this study may prevent disease progression and metastases and potentially improve clinical outcomes by recommending customized treatment approaches for BC patients.
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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 Socratic graph.