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

dc.contributor.authorAbimiku, Aka Patience
dc.contributor.authorAimufua, Gilbert Imuetinyan Osaze
dc.contributor.authorAbdullahi, M.U.
dc.date.accessioned2023-12-14T07:05:06Z
dc.date.available2023-12-14T07:05:06Z
dc.date.issued2022-12-04
dc.description.abstractProtein secondary structure prediction from its amino acids is purposely used to evaluate and improve the accuracy of performance as well as drug design and cell functionality. Various approaches for predicting protein secondary structure have been used, each with varying accuracy, vulnerabilities, and strengths. In view of this, this paper is aimed at training a deep neural network with particle swarm optimization and comparing the results with the state of accuracy. Also, the methodology used is basic particle swarm optimization for training a 20-15-15-15-3 deep neural network. The Java programming language and the Spring Boot framework were employed to implement the various application programming interfaces of the model. The dataset acquired after the training of JPred Server 1.2, which included 1349 training sets and 149 test sets, was used in training the model. Following the training, it was discovered that the model had a highest accuracy of 53.18 percent on epoch 140, indicating that this model is not a best fit or an alternative to the current state of the art for the prediction of protein secondary structure.en_US
dc.identifier.citationAimufua, G.I.O. & et al. (2022) Protein secondary structure prediction using deep neural network and particle swarm optimization algorithmsen_US
dc.identifier.urihttps://keffi.nsuk.edu.ng/handle/20.500.14448/5592
dc.language.isoenen_US
dc.publisherDepartment of Computer Science, Nasarawa State University Keffien_US
dc.subjectProtein; Deep Neural Network; Particle Swarm Optimization; Algorithms; Model; Drug Designen_US
dc.titleProtein secondary structure prediction using deep neural network and particle swarm optimization algorithmsen_US
dc.typeArticleen_US

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