Open AccessPredicting Secondary Structure of Protein Using Hybrid of Convolutional Neural Network and Support Vector Machine
Author(s) -
Vincent Michael Sutanto,
Zaki Sukma,
Afiahayati Afiahayati
Publication year - 2021
Publication title -
international journal of intelligent engineering and systems
Language(s) - English
Resource type - Journals
eISSN - 2185-310X
pISSN - 1882-708X
DOI - 10.22266/ijies2021.0228.23
Subject(s) - support vector machine , computer science , convolutional neural network , artificial intelligence , sequence (biology) , protein secondary structure , field (mathematics) , pattern recognition (psychology) , artificial neural network , machine learning , protein function prediction , recurrent neural network , protein function , mathematics , biology , biochemistry , genetics , gene , pure mathematics
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.