Improved Protein Secondary Structure Prediction Using Support Vector Machine With a New Encoding Scheme and an Advanced Tertiary Classifier

2004 ◽  
Vol 3 (4) ◽  
pp. 265-271 ◽  
Author(s):  
H.-J. Hu ◽  
Y. Pan ◽  
R. Harrison ◽  
P.C. Tai
Keyword(s):  
Support Vector Machine ◽  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Protein Secondary Structure ◽  
Support Vector ◽  
Protein Secondary Structure Prediction ◽  
Encoding Scheme

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.

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