A Machine Learning Framework for Melting Curve Analysis: Sequential Binary Encoding and Dual-Model Error Mitigation

As a cornerstone technique in molecular diagnostics, melting curve analysis (MCA) enables cost-effective multiplex detection using widely accessible fluorescent PCR instruments, eliminating the need for expensive sequence-specific probes. Nevertheless, the broader clinical application of MCA faces limitations due to several interpretation challenges, primarily concerning signal noise, baseline drift, and inter-operator variability. To address these limitations, we developed a dual-model machine learning framework trained on 186,138 samples and validated with 25,918 independent samples. The first model performs curve quality control (QC) using a binary XGBoost classifier (500 trees, depth=10) to filter non-informative curves. The second model determines melting temperature (Tm) values via a 151-bit encoded vector spanning 40-85{degrees}C at 0.3{degrees}C resolution. Internal validation demonstrated high accuracy of the framework in the automatic interpretation of MCA results directly from raw data. External validation showed strong concordance with manual interpretation, with 90.5% of discrepant cases supporting the frameworks predictions upon secondary expert review. Five-fold cross-validation on a balanced subset of 28,880 samples achieved an average accuracy of 98.45% (95% CI: 96.84%-100.00%) and an area-under-the-curve (AUC) value of 0.9991 (SD +/- 0.0012). The system maintained consistent performance across four fluorescence channels (FAM/VIC/ROX/CY5) and substantially reduced interpretation time compared with manual methods. In summary, this work establishes a robust and scalable strategy for the automated interpretation of MCA. The proposed framework can be readily integrated with existing PCR platforms, paving the way for standardized, high-throughput, and intelligent MCA-based molecular diagnostics.