Protein secondary structure prediction using deep neural network and particle swarm optimization algorithms

Protein secondary structure prediction from its amino acids is purposely used to evaluate and improve the accuracy of performance as well drug design cell functionality. Various approaches for predicting protein have been used, each with varying accuracy, vulnerabilities, strengths. In view this, this paper aimed at training a deep neural network particle swarm optimization comparing results state accuracy. Also, methodology basic 20-15-15-15-3 network. The Java programming language Spring Boot framework were employed implement various application interfaces model. dataset acquired after JPred Server 1.2, which included 1349 sets 149 test sets, was in Following training, it discovered that model had highest 53.18 percent on epoch 140, indicating not best fit or an alternative current art structure.