Deep learning Support Vector Machine Application Management for Prediction Binding Elements

Considering that alkaline earth metals particles are key protein binding partners in the biological system, predicting their binding positions is of critical importance. The study objectives in this work are the compounds of the metal ions Mg2+ or Ca2+. Deep learning models' algorithms are utilized to estimate the binding locations of proteins depending on the distinctive properties of protein sequencing, amino acids, physicochemical qualities of amino acids, or projected structure data. The predictions outcomes obtained via fivefold cross-validation are superior to those obtained by the Ion-seq approach because the hyper-parameters of the deep learning model have been optimized. As a bonus, the under-sampling information treatment approach is used to properly evaluate and validate the efficiency of the presented models, as well as the predictions findings acquired via independently testing are superior to those produced by the SVM classifier.