TY - GEN
T1 - Prediction of Protein-Protein Interactions through Deep Learning Based on Sequence Feature Extraction and Interaction Network
AU - Le, Nguyen Quoc Khanh
AU - Kha, Quang Hien
N1 - Funding Information:
This work was supported by the Ministry of Science and Technology, Taiwan [grant number MOST110-2221-E-038-001-MY2] and the Taiwan Higher Education Sprout Project by the Ministry of Education [grant number DP2-111-21121-01-A-12]
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Protein-protein interaction (PPI) is an important molecular process in the cell, which is vital to the function of the cell in the biochemical process. This study focuses on human protein. It uses protein information and the relationship of protein interaction network structure to predict PPI. Deep neural network model is implemented to realize PPI prediction. Through five-fold cross-validation, a high performance in the prediction accuracy is produced. The accuracy rate on the test set is 92.45%. To further evaluate the performance of this method, we compared it with other machine learning algorithms. The experimental results show that the method based on neural network is significantly better than the others on the same dataset. It also shows a superior performance compared to previous predictors in this field on PPI prediction.
AB - Protein-protein interaction (PPI) is an important molecular process in the cell, which is vital to the function of the cell in the biochemical process. This study focuses on human protein. It uses protein information and the relationship of protein interaction network structure to predict PPI. Deep neural network model is implemented to realize PPI prediction. Through five-fold cross-validation, a high performance in the prediction accuracy is produced. The accuracy rate on the test set is 92.45%. To further evaluate the performance of this method, we compared it with other machine learning algorithms. The experimental results show that the method based on neural network is significantly better than the others on the same dataset. It also shows a superior performance compared to previous predictors in this field on PPI prediction.
KW - deep learning
KW - neural network
KW - protein-protein interaction
KW - sequence information
KW - topological information extraction
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U2 - 10.1109/BioCAS54905.2022.9948611
DO - 10.1109/BioCAS54905.2022.9948611
M3 - Conference contribution
AN - SCOPUS:85142926631
T3 - BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference: Intelligent Biomedical Systems for a Better Future, Proceedings
SP - 539
EP - 543
BT - BioCAS 2022 - IEEE Biomedical Circuits and Systems Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE Biomedical Circuits and Systems Conference, BioCAS 2022
Y2 - 13 October 2022 through 15 October 2022
ER -