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Accelerating drug repurposing for COVID-19 treatment by modeling mechanisms of action using cell image features and machine learning
The novel coronavirus disease, COVID-19, has rapidly spread worldwide. Developing methods to identify the therapeutic activity of drugs based on phenotypic data can improve the efficiency of drug development. Here, a state-of-the-art machine-learning method was used to identify drug mechanism of act...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8570398/ https://www.ncbi.nlm.nih.gov/pubmed/34777628 http://dx.doi.org/10.1007/s11571-021-09727-5 |
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author | Han, Lu Shan, Guangcun Chu, Bingfeng Wang, Hongyu Wang, Zhongjian Gao, Shengqiao Zhou, Wenxia |
author_facet | Han, Lu Shan, Guangcun Chu, Bingfeng Wang, Hongyu Wang, Zhongjian Gao, Shengqiao Zhou, Wenxia |
author_sort | Han, Lu |
collection | PubMed |
description | The novel coronavirus disease, COVID-19, has rapidly spread worldwide. Developing methods to identify the therapeutic activity of drugs based on phenotypic data can improve the efficiency of drug development. Here, a state-of-the-art machine-learning method was used to identify drug mechanism of actions (MoAs) based on the cell image features of 1105 drugs in the LINCS database. As the multi-dimensional features of cell images are affected by non-experimental factors, the characteristics of similar drugs vary considerably, and it is difficult to effectively identify the MoA of drugs as there is substantial noise. By applying the supervised information theoretic metric-learning (ITML) algorithm, a linear transformation made drugs with the same MoA aggregate. By clustering drugs to communities and performing enrichment analysis, we found that transferred image features were more conducive to the recognition of drug MoAs. Image features analysis showed that different features play important roles in identifying different drug functions. Drugs that significantly affect cell survival or proliferation, such as cyclin-dependent kinase inhibitors, were more likely to be enriched in communities, whereas other drugs might be decentralized. Chloroquine and clomiphene, which block the entry of virus, were clustered into the same community, indicating that similar MoA could be reflected by the cell image. Overall, the findings of the present study laid the foundation for the discovery of MoAs of new drugs, based on image data. In addition, it provided a new method of drug repurposing for COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-021-09727-5. |
format | Online Article Text |
id | pubmed-8570398 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-85703982021-11-08 Accelerating drug repurposing for COVID-19 treatment by modeling mechanisms of action using cell image features and machine learning Han, Lu Shan, Guangcun Chu, Bingfeng Wang, Hongyu Wang, Zhongjian Gao, Shengqiao Zhou, Wenxia Cogn Neurodyn Research Article The novel coronavirus disease, COVID-19, has rapidly spread worldwide. Developing methods to identify the therapeutic activity of drugs based on phenotypic data can improve the efficiency of drug development. Here, a state-of-the-art machine-learning method was used to identify drug mechanism of actions (MoAs) based on the cell image features of 1105 drugs in the LINCS database. As the multi-dimensional features of cell images are affected by non-experimental factors, the characteristics of similar drugs vary considerably, and it is difficult to effectively identify the MoA of drugs as there is substantial noise. By applying the supervised information theoretic metric-learning (ITML) algorithm, a linear transformation made drugs with the same MoA aggregate. By clustering drugs to communities and performing enrichment analysis, we found that transferred image features were more conducive to the recognition of drug MoAs. Image features analysis showed that different features play important roles in identifying different drug functions. Drugs that significantly affect cell survival or proliferation, such as cyclin-dependent kinase inhibitors, were more likely to be enriched in communities, whereas other drugs might be decentralized. Chloroquine and clomiphene, which block the entry of virus, were clustered into the same community, indicating that similar MoA could be reflected by the cell image. Overall, the findings of the present study laid the foundation for the discovery of MoAs of new drugs, based on image data. In addition, it provided a new method of drug repurposing for COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-021-09727-5. Springer Netherlands 2021-11-05 2023-06 /pmc/articles/PMC8570398/ /pubmed/34777628 http://dx.doi.org/10.1007/s11571-021-09727-5 Text en © The Author(s), under exclusive licence to Springer Nature B.V. 2021 |
spellingShingle | Research Article Han, Lu Shan, Guangcun Chu, Bingfeng Wang, Hongyu Wang, Zhongjian Gao, Shengqiao Zhou, Wenxia Accelerating drug repurposing for COVID-19 treatment by modeling mechanisms of action using cell image features and machine learning |
title | Accelerating drug repurposing for COVID-19 treatment by modeling mechanisms of action using cell image features and machine learning |
title_full | Accelerating drug repurposing for COVID-19 treatment by modeling mechanisms of action using cell image features and machine learning |
title_fullStr | Accelerating drug repurposing for COVID-19 treatment by modeling mechanisms of action using cell image features and machine learning |
title_full_unstemmed | Accelerating drug repurposing for COVID-19 treatment by modeling mechanisms of action using cell image features and machine learning |
title_short | Accelerating drug repurposing for COVID-19 treatment by modeling mechanisms of action using cell image features and machine learning |
title_sort | accelerating drug repurposing for covid-19 treatment by modeling mechanisms of action using cell image features and machine learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8570398/ https://www.ncbi.nlm.nih.gov/pubmed/34777628 http://dx.doi.org/10.1007/s11571-021-09727-5 |
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