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Protocol for vision transformer-based evaluation of drug potency using images processed by an optimized Sobel operator

Conventional approaches for screening anticancer drugs rely on chemical reactions, which are time consuming, labor intensive, and costly. Here, we present a protocol for label-free and high-throughput assessment of drug efficacy using a vision transformer and a Conv2D. We describe the steps for cell...

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Detalles Bibliográficos
Autores principales: Wang, Yongheng, Zhang, Weidi, Wu, Yi, Qu, Chuyuan, Hu, Hongru, Lee, Teresa, Lin, Siyu, Zhang, Jiawei, Lam, Kit S., Wang, Aijun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10176075/
https://www.ncbi.nlm.nih.gov/pubmed/37133992
http://dx.doi.org/10.1016/j.xpro.2023.102259
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author Wang, Yongheng
Zhang, Weidi
Wu, Yi
Qu, Chuyuan
Hu, Hongru
Lee, Teresa
Lin, Siyu
Zhang, Jiawei
Lam, Kit S.
Wang, Aijun
author_facet Wang, Yongheng
Zhang, Weidi
Wu, Yi
Qu, Chuyuan
Hu, Hongru
Lee, Teresa
Lin, Siyu
Zhang, Jiawei
Lam, Kit S.
Wang, Aijun
author_sort Wang, Yongheng
collection PubMed
description Conventional approaches for screening anticancer drugs rely on chemical reactions, which are time consuming, labor intensive, and costly. Here, we present a protocol for label-free and high-throughput assessment of drug efficacy using a vision transformer and a Conv2D. We describe the steps for cell culture, drug treatment, data collection, and preprocessing. We then detail the building of deep learning models and their use to predict drug potency. This protocol can be adapted for screening chemicals that affect the density or morphological features of cells. For complete details on the use and execution of this protocol, please refer to Wang et al.(1)
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spelling pubmed-101760752023-05-13 Protocol for vision transformer-based evaluation of drug potency using images processed by an optimized Sobel operator Wang, Yongheng Zhang, Weidi Wu, Yi Qu, Chuyuan Hu, Hongru Lee, Teresa Lin, Siyu Zhang, Jiawei Lam, Kit S. Wang, Aijun STAR Protoc Protocol Conventional approaches for screening anticancer drugs rely on chemical reactions, which are time consuming, labor intensive, and costly. Here, we present a protocol for label-free and high-throughput assessment of drug efficacy using a vision transformer and a Conv2D. We describe the steps for cell culture, drug treatment, data collection, and preprocessing. We then detail the building of deep learning models and their use to predict drug potency. This protocol can be adapted for screening chemicals that affect the density or morphological features of cells. For complete details on the use and execution of this protocol, please refer to Wang et al.(1) Elsevier 2023-05-01 /pmc/articles/PMC10176075/ /pubmed/37133992 http://dx.doi.org/10.1016/j.xpro.2023.102259 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Wang, Yongheng
Zhang, Weidi
Wu, Yi
Qu, Chuyuan
Hu, Hongru
Lee, Teresa
Lin, Siyu
Zhang, Jiawei
Lam, Kit S.
Wang, Aijun
Protocol for vision transformer-based evaluation of drug potency using images processed by an optimized Sobel operator
title Protocol for vision transformer-based evaluation of drug potency using images processed by an optimized Sobel operator
title_full Protocol for vision transformer-based evaluation of drug potency using images processed by an optimized Sobel operator
title_fullStr Protocol for vision transformer-based evaluation of drug potency using images processed by an optimized Sobel operator
title_full_unstemmed Protocol for vision transformer-based evaluation of drug potency using images processed by an optimized Sobel operator
title_short Protocol for vision transformer-based evaluation of drug potency using images processed by an optimized Sobel operator
title_sort protocol for vision transformer-based evaluation of drug potency using images processed by an optimized sobel operator
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10176075/
https://www.ncbi.nlm.nih.gov/pubmed/37133992
http://dx.doi.org/10.1016/j.xpro.2023.102259
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