Integrating CCK8 and qPCR with xAI Models to Evaluate siRNA Effects on Cellular Viability and Gene Expression

The proposed work evaluates the performance of five machine learning algorithms XGBoost, RandomForest, GradientBoosting, Support Vector Machine (SVM), and Logistic Regression in predicting the effects of small interfering RNA (siRNA) on cellular viability and gene expression. Model performance was assessed using accuracy, precision, recall, F1-score, and feature importance, with interpretability facilitated by Shapley Additive Explanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). XGBoost emerged as the most effective model, achieving 75% accuracy with robust identification of Class 0, though its precision for Class 1 remained suboptimal. GradientBoosting exhibited comparable accuracy but did not surpass XGBoost. RandomForest and Logistic Regression underperformed significantly, each achieving only 25% accuracy, while SVM demonstrated moderate performance with 50% accuracy, reflecting its limitations in handling non-linearities. SHAP and LIME analyses consistently identified GAPDH, NC siRNA, and ACP6 siRNA as critical features influencing model predictions. Despite XGBoost's strong performance, challenges such as class imbalance and limited interpretability were evident. Future work will prioritize hyperparameter optimization, advanced feature engineering, and techniques like SMOTE to address class imbalance. These findings underscore the potential of machine learning in evaluating siRNA-mediated effects while highlighting areas for methodological refinement to enhance predictive accuracy and interpretability in gene expression analysis.