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AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders

BACKGROUND: Drug–target interaction (DTI) plays a vital role in drug discovery. Identifying drug–target interactions related to wet-lab experiments are costly, laborious, and time-consuming. Therefore, computational methods to predict drug–target interactions are an essential task in the drug discov...

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Autores principales: Sajadi, Seyedeh Zahra, Zare Chahooki, Mohammad Ali, Gharaghani, Sajjad, Abbasi, Karim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056558/
https://www.ncbi.nlm.nih.gov/pubmed/33879050
http://dx.doi.org/10.1186/s12859-021-04127-2
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author Sajadi, Seyedeh Zahra
Zare Chahooki, Mohammad Ali
Gharaghani, Sajjad
Abbasi, Karim
author_facet Sajadi, Seyedeh Zahra
Zare Chahooki, Mohammad Ali
Gharaghani, Sajjad
Abbasi, Karim
author_sort Sajadi, Seyedeh Zahra
collection PubMed
description BACKGROUND: Drug–target interaction (DTI) plays a vital role in drug discovery. Identifying drug–target interactions related to wet-lab experiments are costly, laborious, and time-consuming. Therefore, computational methods to predict drug–target interactions are an essential task in the drug discovery process. Meanwhile, computational methods can reduce search space by proposing potential drugs already validated on wet-lab experiments. Recently, deep learning-based methods in drug-target interaction prediction have gotten more attention. Traditionally, DTI prediction methods' performance heavily depends on additional information, such as protein sequence and molecular structure of the drug, as well as deep supervised learning. RESULTS: This paper proposes a method based on deep unsupervised learning for drug-target interaction prediction called AutoDTI++. The proposed method includes three steps. The first step is to pre-process the interaction matrix. Since the interaction matrix is sparse, we solved the sparsity of the interaction matrix with drug fingerprints. Then, in the second step, the AutoDTI approach is introduced. In the third step, we post-preprocess the output of the AutoDTI model. CONCLUSIONS: Experimental results have shown that we were able to improve the prediction performance. To this end, the proposed method has been compared to other algorithms using the same reference datasets. The proposed method indicates that the experimental results of running five repetitions of tenfold cross-validation on golden standard datasets (Nuclear Receptors, GPCRs, Ion channels, and Enzymes) achieve good performance with high accuracy.
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spelling pubmed-80565582021-04-20 AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders Sajadi, Seyedeh Zahra Zare Chahooki, Mohammad Ali Gharaghani, Sajjad Abbasi, Karim BMC Bioinformatics Research BACKGROUND: Drug–target interaction (DTI) plays a vital role in drug discovery. Identifying drug–target interactions related to wet-lab experiments are costly, laborious, and time-consuming. Therefore, computational methods to predict drug–target interactions are an essential task in the drug discovery process. Meanwhile, computational methods can reduce search space by proposing potential drugs already validated on wet-lab experiments. Recently, deep learning-based methods in drug-target interaction prediction have gotten more attention. Traditionally, DTI prediction methods' performance heavily depends on additional information, such as protein sequence and molecular structure of the drug, as well as deep supervised learning. RESULTS: This paper proposes a method based on deep unsupervised learning for drug-target interaction prediction called AutoDTI++. The proposed method includes three steps. The first step is to pre-process the interaction matrix. Since the interaction matrix is sparse, we solved the sparsity of the interaction matrix with drug fingerprints. Then, in the second step, the AutoDTI approach is introduced. In the third step, we post-preprocess the output of the AutoDTI model. CONCLUSIONS: Experimental results have shown that we were able to improve the prediction performance. To this end, the proposed method has been compared to other algorithms using the same reference datasets. The proposed method indicates that the experimental results of running five repetitions of tenfold cross-validation on golden standard datasets (Nuclear Receptors, GPCRs, Ion channels, and Enzymes) achieve good performance with high accuracy. BioMed Central 2021-04-20 /pmc/articles/PMC8056558/ /pubmed/33879050 http://dx.doi.org/10.1186/s12859-021-04127-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Sajadi, Seyedeh Zahra
Zare Chahooki, Mohammad Ali
Gharaghani, Sajjad
Abbasi, Karim
AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders
title AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders
title_full AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders
title_fullStr AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders
title_full_unstemmed AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders
title_short AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders
title_sort autodti++: deep unsupervised learning for dti prediction by autoencoders
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8056558/
https://www.ncbi.nlm.nih.gov/pubmed/33879050
http://dx.doi.org/10.1186/s12859-021-04127-2
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