ATMeQ: A Machine Learning-Based Framework for Amyotrophic Lateral Sclerosis Disease using RNA-seq Meta-Analysis

methods Random Forest importance, Gradient Boosting, Recursive Feature Elimination (RFE), and the Boruta algorithm, narrowed this set down to a biologically meaningful six-gene signature (ACTA1, ABCA4, COL6A4P2, HERC2P2, KCNE4, LOC107987008). Employing this signature, fifteen machine learning models were trained and optimized through hyperparameter tuning. The top-performing model, a Gradient Boosting Classifier (GBC), was validated through k-fold cross-validation, achieving 96% accuracy, a 0.92 Matthews Correlation Coefficient (MCC), 0.937 precision, 0.991 recall, 0.962 F1-score, and a 0.993 AUC-ROC. Therefore, this model was deployed as ATMeQ, a publicly available web tool (https://atmeq-ai.streamlit.app/) with potential utility for clinicians and researchers to predict ALS risk and validate biomarkers. Collectively, the study demonstrates that integrative transcriptomics and machine learning can significantly reduce potential diagnostic delays and enable biomarker-driven detection in ALS.