scholarly journals Protein secondary structure prediction with context convolutional neural network

RSC Advances ◽  
2019 ◽  
Vol 9 (66) ◽  
pp. 38391-38396 ◽  
Author(s):  
Shiyang Long ◽  
Pu Tian
Keyword(s):  
Neural Network ◽  
Secondary Structure ◽  
Convolutional Neural Network ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Protein Secondary Structure Prediction ◽  
P Protein

Protein secondary structure prediction using context convolutional neural network.

scholarly journals Predicting Secondary Structure of Protein Using Hybrid of Convolutional Neural Network and Support Vector Machine

2021 ◽  
Vol 14 (1) ◽  
pp. 232-243
Author(s):  
Vincent Sutanto ◽  
◽  
Zaki Sukma ◽  
Afiahayati Afiahayati ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Secondary Structure ◽  
Convolutional Neural Network ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Support Vector ◽  
Classification Problems ◽  
Protein Secondary Structure Prediction

Protein secondary structure prediction is one of the problems in the Bioinformatics field, which conducted to find the function of proteins. Protein secondary structure prediction is done by classifying each sequence of protein primary structure into the sequence of protein secondary structure, which fall in sequence labelling problems and can be solved with the machine learning. Convolutional Neural Network (CNN) and Support Vector Machine (SVM) are 2 methods that often used to solve classification problems. In this research, we proposed a hybrid of 1-Dimensional CNN and SVM to predict the secondary structure of the protein. In this research, we used a novel hybrid 1-Dimensional CNN and SVM for sequence labelling, specifically to predict the secondary structure of the protein. Our hybrid model managed to outperform previous studies in term of Q3 and Q8 accuracy on CB513 dataset.

scholarly journals OCLSTM: Optimized convolutional and long short-term memory neural network model for protein secondary structure prediction

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0245982
Author(s):  
Yawu Zhao ◽  
Yihui Liu
Keyword(s):  
Neural Network ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Short Term Memory ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Short Term ◽  
Protein Secondary Structure Prediction ◽  
Term Memory ◽  
Long Short Term Memory

Protein secondary structure prediction is extremely important for determining the spatial structure and function of proteins. In this paper, we apply an optimized convolutional neural network and long short-term memory neural network models to protein secondary structure prediction, which is called OCLSTM. We use an optimized convolutional neural network to extract local features between amino acid residues. Then use the bidirectional long short-term memory neural network to extract the remote interactions between the internal residues of the protein sequence to predict the protein structure. Experiments are performed on CASP10, CASP11, CASP12, CB513, and 25PDB datasets, and the good performance of 84.68%, 82.36%, 82.91%, 84.21% and 85.08% is achieved respectively. Experimental results show that the model can achieve better results.

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