VOLUME 17 NUMBER 2 (July to December 2024)

Development of machine learning algorithm for loop-mediated isothermal amplification including influence of temperature

To predict amplification in the Loop-Mediated Isothermal Amplification (LAMP) method, a binary model has been developed. This model assesses whether the combination of partial template binding facilitated by multiple primers aligns with a specific signature indicating amplification. For this model, 1512 settings were adjusted, encompassing variations in the extraction oligomer position from the primer, the oligomer length, the number of allowed mismatched bases, and the stringency of the homology position. The optimal set was determined based on the F1 value at a reaction temperature of 60°C, resulting in a value of 0.58. To implement machine learning by amalgamating multiple models, duplicate models were eliminated through cluster analysis. Machine learning using AutoML was performed on 108 distinct models. Consequently, the F1 values were 0.7119 at a reaction temperature of 60°C and 0.7210 at 68°C. The weak correlation between feature importance and the F1 value of the model alone suggests that machine learning utilizing this model incorporates numerous models to enhance prediction accuracy.