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Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning

Drug repositioning, which involves the identification of new therapeutic indications for approved drugs, considerably reduces the time and cost of developing new drugs. Recent computational drug repositioning methods use heterogeneous networks to identify drug–disease associations. This review revea...

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Autores principales: Kim, Yoonbee, Jung, Yi-Sue, Park, Jong-Hoon, Kim, Seon-Jun, Cho, Young-Rae
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9599692/
https://www.ncbi.nlm.nih.gov/pubmed/36291706
http://dx.doi.org/10.3390/biom12101497
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author Kim, Yoonbee
Jung, Yi-Sue
Park, Jong-Hoon
Kim, Seon-Jun
Cho, Young-Rae
author_facet Kim, Yoonbee
Jung, Yi-Sue
Park, Jong-Hoon
Kim, Seon-Jun
Cho, Young-Rae
author_sort Kim, Yoonbee
collection PubMed
description Drug repositioning, which involves the identification of new therapeutic indications for approved drugs, considerably reduces the time and cost of developing new drugs. Recent computational drug repositioning methods use heterogeneous networks to identify drug–disease associations. This review reveals existing network-based approaches for predicting drug–disease associations in three major categories: graph mining, matrix factorization or completion, and deep learning. We selected eleven methods from the three categories to compare their predictive performances. The experiment was conducted using two uniform datasets on the drug and disease sides, separately. We constructed heterogeneous networks using drug–drug similarities based on chemical structures and ATC codes, ontology-based disease–disease similarities, and drug–disease associations. An improved evaluation metric was used to reflect data imbalance as positive associations are typically sparse. The prediction results demonstrated that methods in the graph mining and matrix factorization or completion categories performed well in the overall assessment. Furthermore, prediction on the drug side had higher accuracy than on the disease side. Selecting and integrating informative drug features in drug–drug similarity measurement are crucial for improving disease-side prediction.
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spelling pubmed-95996922022-10-27 Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning Kim, Yoonbee Jung, Yi-Sue Park, Jong-Hoon Kim, Seon-Jun Cho, Young-Rae Biomolecules Review Drug repositioning, which involves the identification of new therapeutic indications for approved drugs, considerably reduces the time and cost of developing new drugs. Recent computational drug repositioning methods use heterogeneous networks to identify drug–disease associations. This review reveals existing network-based approaches for predicting drug–disease associations in three major categories: graph mining, matrix factorization or completion, and deep learning. We selected eleven methods from the three categories to compare their predictive performances. The experiment was conducted using two uniform datasets on the drug and disease sides, separately. We constructed heterogeneous networks using drug–drug similarities based on chemical structures and ATC codes, ontology-based disease–disease similarities, and drug–disease associations. An improved evaluation metric was used to reflect data imbalance as positive associations are typically sparse. The prediction results demonstrated that methods in the graph mining and matrix factorization or completion categories performed well in the overall assessment. Furthermore, prediction on the drug side had higher accuracy than on the disease side. Selecting and integrating informative drug features in drug–drug similarity measurement are crucial for improving disease-side prediction. MDPI 2022-10-17 /pmc/articles/PMC9599692/ /pubmed/36291706 http://dx.doi.org/10.3390/biom12101497 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Kim, Yoonbee
Jung, Yi-Sue
Park, Jong-Hoon
Kim, Seon-Jun
Cho, Young-Rae
Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning
title Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning
title_full Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning
title_fullStr Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning
title_full_unstemmed Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning
title_short Drug-Disease Association Prediction Using Heterogeneous Networks for Computational Drug Repositioning
title_sort drug-disease association prediction using heterogeneous networks for computational drug repositioning
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9599692/
https://www.ncbi.nlm.nih.gov/pubmed/36291706
http://dx.doi.org/10.3390/biom12101497
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