TY - GEN
T1 - Drug-target interaction prediction in coronavirus disease 2019 case using deep semi-supervised learning model
AU - Sulistiawan, Faldi
AU - Kusuma, Wisnu Ananta
AU - Ramadhanti, Nabila Sekar
AU - Tedjo, Aryo
N1 - Funding Information:
This research is supported by Ministry of Research, Technology and Higher Education, Indonesia, under Competitive Research Grant from Directorate of Higher Education, Indonesia, 2020, contract no. 4020/IT3.L1/PN/2020.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/10/17
Y1 - 2020/10/17
N2 - Coronavirus disease 2019 (COVID-19) is an infectious disease of the respiratory system that caused a pandemic in 2020. There is still not any effective special treatment to cure it. Drug repositioning is used to find an effective drug for curing new diseases by finding new efficacy of registered drug. The new efficacy can be conducted by elaborating the interactions between compounds and proteins (DTI). Deep Semi-Supervised Learning (DSSL) is used to overcome the lack of DTI information. DSSL utilizes unsupervised learning algorithms such as Stacked Auto Encoder (SAE) as pre-training for initializing weights on the Deep Neural Network (DNN). This study uses DSSL with a feature-based chemogenomics approach on the data resulted from the exploration of potential anti-coronavirus treatment. This study finds that the use of fingerprints for compound features and Dipeptide Composition (DC) for protein features gives the best results on accuracy (0.94), recall (0.83), precision (0.817), F-measure (0.822), and AUROC (0.97). From the test data predictions, 1766 and 929 positive interactions are found on the test data and herbal compounds, respectively.
AB - Coronavirus disease 2019 (COVID-19) is an infectious disease of the respiratory system that caused a pandemic in 2020. There is still not any effective special treatment to cure it. Drug repositioning is used to find an effective drug for curing new diseases by finding new efficacy of registered drug. The new efficacy can be conducted by elaborating the interactions between compounds and proteins (DTI). Deep Semi-Supervised Learning (DSSL) is used to overcome the lack of DTI information. DSSL utilizes unsupervised learning algorithms such as Stacked Auto Encoder (SAE) as pre-training for initializing weights on the Deep Neural Network (DNN). This study uses DSSL with a feature-based chemogenomics approach on the data resulted from the exploration of potential anti-coronavirus treatment. This study finds that the use of fingerprints for compound features and Dipeptide Composition (DC) for protein features gives the best results on accuracy (0.94), recall (0.83), precision (0.817), F-measure (0.822), and AUROC (0.97). From the test data predictions, 1766 and 929 positive interactions are found on the test data and herbal compounds, respectively.
KW - Coronavirus disease 2019
KW - Deep neural network
KW - Deep semi-supervised learning
KW - Drug repositioning
KW - Stacked autoencoder
UR - http://www.scopus.com/inward/record.url?scp=85099758778&partnerID=8YFLogxK
U2 - 10.1109/ICACSIS51025.2020.9263241
DO - 10.1109/ICACSIS51025.2020.9263241
M3 - Conference contribution
AN - SCOPUS:85099758778
T3 - 2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
SP - 83
EP - 88
BT - 2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020
Y2 - 17 October 2020 through 18 October 2020
ER -
