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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9599692 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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