Moheb R. Girgis, Rofida M. Gamal, Enas Elgeldawi^*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3951-3967, 2022, DOI:10.32604/cmc.2022.030934

Abstract Protein structure prediction is one of the most essential objectives practiced by theoretical chemistry and bioinformatics as it is of a vital importance in medicine, biotechnology and more. Protein secondary structure prediction (PSSP) has a significant role in the prediction of protein tertiary structure, as it bridges the gap between the protein primary sequences and tertiary structure prediction. Protein secondary structures are classified into two categories: 3-state category and 8-state category. Predicting the 3 states and the 8 states of secondary structures from protein sequences are called the Q3 prediction and the Q8 prediction problems, respectively. The 8 classes of… More >

Muhammad Zubair¹, Muhammad Kashif Hanif^1,*, Eatedal Alabdulkreem², Yazeed Ghadi³, Muhammad Irfan Khan¹, Muhammad Umer Sarwar¹, Ayesha Hanif¹

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3705-3718, 2022, DOI:10.32604/cmc.2022.026408

Abstract The secondary structure of a protein is critical for establishing a link between the protein primary and tertiary structures. For this reason, it is important to design methods for accurate protein secondary structure prediction. Most of the existing computational techniques for protein structural and functional prediction are based on machine learning with shallow frameworks. Different deep learning architectures have already been applied to tackle protein secondary structure prediction problem. In this study, deep learning based models, i.e., convolutional neural network and long short-term memory for protein secondary structure prediction were proposed. The input to proposed models is amino acid sequences… More >

Yuehan Du^1,2, Ruoyu Zhang¹, Xu Zhang¹, Antai Ouyang³, Xiaodong Zhang⁴, Jinyong Cheng¹, Wenpeng Lu^1,*

Journal of Quantum Computing, Vol.1, No.1, pp. 21-28, 2019, DOI:10.32604/jqc.2019.06063

Abstract The algorithm based on combination learning usually is superior to a single classification algorithm on the task of protein secondary structure prediction. However, the assignment of the weight of the base classifier usually lacks decision-making evidence. In this paper, we propose a protein secondary structure prediction method with dynamic self-adaptation combination strategy based on entropy, where the weights are assigned according to the entropy of posterior probabilities outputted by base classifiers. The higher entropy value means a lower weight for the base classifier. The final structure prediction is decided by the weighted combination of posterior probabilities. Extensive experiments on CB513… More >