| Anova | Analysis of Variance |
| AP | Average precision |
| AUC | Area Under the Receiver Operating Characteristic Curve |
| CCAAT | Cytosine–Cytosine–Adenosine–Adenosine–Thymidine |
| cfDNA | Cell-Free Deoxyribonucleic Acid |
| cfRNA | Cell-Free Ribonucleic Acid |
| DNA | Deoxyribonucleic Acid |
| DNN | Deep Neural Network |
| DT | Decision Tree |
| EMT | Epithelial–Mesenchymal Transition |
| eQTL | Expression Quantitative Trait Locus |
| GEO | Gene Expression Omnibus |
| HPA | The Human Protein Atlas |
| IQR | Interquartile Range |
| LIME | Local Interpretable Model-Agnostic Explanations |
| LR | Logistic Regression |
| MCED | Multi-Cancer Early Detection |
| ML | Machine Learning |
| NN | Neural Network |
| PBMC | Peripheral Blood Mononuclear Cell |
| ReLU | Rectified Linear Unit |
| RF | Random Forest |
| RNA | Ribonucleic Acid |
| ROC | Receiver Operating Characteristic |
| SHAP | SHapley Additive exPlanations |
| SLSQP | Sequential Least Squares Quadratic Programming |
| SMOTE | Synthetic Minority Over-sampling Technique |
| SVM | Support Vector Machine |
| TCGA | The Cancer Genome Atlas |
| TEP | Tumor-Educated Platelets |
| XAI | Explainable Artificial Intelligence |
| XGB | Extreme Gradient Boosting |