Cargando…
Drug repositioning based on similarity constrained probabilistic matrix factorization: COVID-19 as a case study
The novel coronavirus disease 2019 (COVID-19) pandemic has caused a massive health crisis worldwide and upended the global economy. However, vaccines and traditional drug discovery for COVID-19 cost too much in terms of time, manpower, and money. Drug repurposing becomes one of the promising treatme...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7825831/ https://www.ncbi.nlm.nih.gov/pubmed/33519322 http://dx.doi.org/10.1016/j.asoc.2021.107135 |
_version_ | 1783640399885107200 |
---|---|
author | Meng, Yajie Jin, Min Tang, Xianfang Xu, Junlin |
author_facet | Meng, Yajie Jin, Min Tang, Xianfang Xu, Junlin |
author_sort | Meng, Yajie |
collection | PubMed |
description | The novel coronavirus disease 2019 (COVID-19) pandemic has caused a massive health crisis worldwide and upended the global economy. However, vaccines and traditional drug discovery for COVID-19 cost too much in terms of time, manpower, and money. Drug repurposing becomes one of the promising treatment strategies amid the COVID-19 crisis. At present, there are no publicly existing databases for experimentally supported human drug–virus interactions, and most existing drug repurposing methods require the rich information, which is not always available, especially for a new virus. In this study, on the one hand, we put size-able efforts to collect drug–virus interaction entries from literature and build the Human Drug Virus Database (HDVD). On the other hand, we propose a new approach, called SCPMF (similarity constrained probabilistic matrix factorization), to identify new drug–virus interactions for drug repurposing. SCPMF is implemented on an adjacency matrix of a heterogeneous drug–virus network, which integrates the known drug–virus interactions, drug chemical structures, and virus genomic sequences. SCPMF projects the drug–virus interactions matrix into two latent feature matrices for the drugs and viruses, which reconstruct the drug–virus interactions matrix when multiplied together, and then introduces the weighted similarity interaction matrix as constraints for drugs and viruses. Benchmarking comparisons on two different datasets demonstrate that SCPMF has reliable prediction performance and outperforms several recent approaches. Moreover, SCPMF-predicted drug candidates of COVID-19 also confirm the accuracy and reliability of SCPMF. |
format | Online Article Text |
id | pubmed-7825831 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78258312021-01-25 Drug repositioning based on similarity constrained probabilistic matrix factorization: COVID-19 as a case study Meng, Yajie Jin, Min Tang, Xianfang Xu, Junlin Appl Soft Comput Article The novel coronavirus disease 2019 (COVID-19) pandemic has caused a massive health crisis worldwide and upended the global economy. However, vaccines and traditional drug discovery for COVID-19 cost too much in terms of time, manpower, and money. Drug repurposing becomes one of the promising treatment strategies amid the COVID-19 crisis. At present, there are no publicly existing databases for experimentally supported human drug–virus interactions, and most existing drug repurposing methods require the rich information, which is not always available, especially for a new virus. In this study, on the one hand, we put size-able efforts to collect drug–virus interaction entries from literature and build the Human Drug Virus Database (HDVD). On the other hand, we propose a new approach, called SCPMF (similarity constrained probabilistic matrix factorization), to identify new drug–virus interactions for drug repurposing. SCPMF is implemented on an adjacency matrix of a heterogeneous drug–virus network, which integrates the known drug–virus interactions, drug chemical structures, and virus genomic sequences. SCPMF projects the drug–virus interactions matrix into two latent feature matrices for the drugs and viruses, which reconstruct the drug–virus interactions matrix when multiplied together, and then introduces the weighted similarity interaction matrix as constraints for drugs and viruses. Benchmarking comparisons on two different datasets demonstrate that SCPMF has reliable prediction performance and outperforms several recent approaches. Moreover, SCPMF-predicted drug candidates of COVID-19 also confirm the accuracy and reliability of SCPMF. Elsevier B.V. 2021-05 2021-01-23 /pmc/articles/PMC7825831/ /pubmed/33519322 http://dx.doi.org/10.1016/j.asoc.2021.107135 Text en © 2021 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Meng, Yajie Jin, Min Tang, Xianfang Xu, Junlin Drug repositioning based on similarity constrained probabilistic matrix factorization: COVID-19 as a case study |
title | Drug repositioning based on similarity constrained probabilistic matrix factorization: COVID-19 as a case study |
title_full | Drug repositioning based on similarity constrained probabilistic matrix factorization: COVID-19 as a case study |
title_fullStr | Drug repositioning based on similarity constrained probabilistic matrix factorization: COVID-19 as a case study |
title_full_unstemmed | Drug repositioning based on similarity constrained probabilistic matrix factorization: COVID-19 as a case study |
title_short | Drug repositioning based on similarity constrained probabilistic matrix factorization: COVID-19 as a case study |
title_sort | drug repositioning based on similarity constrained probabilistic matrix factorization: covid-19 as a case study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7825831/ https://www.ncbi.nlm.nih.gov/pubmed/33519322 http://dx.doi.org/10.1016/j.asoc.2021.107135 |
work_keys_str_mv | AT mengyajie drugrepositioningbasedonsimilarityconstrainedprobabilisticmatrixfactorizationcovid19asacasestudy AT jinmin drugrepositioningbasedonsimilarityconstrainedprobabilisticmatrixfactorizationcovid19asacasestudy AT tangxianfang drugrepositioningbasedonsimilarityconstrainedprobabilisticmatrixfactorizationcovid19asacasestudy AT xujunlin drugrepositioningbasedonsimilarityconstrainedprobabilisticmatrixfactorizationcovid19asacasestudy |