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基于视觉变换器的药物效力评估协议,使用经优化的Sobel算子处理的图像。

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

作者信息

Wang Yongheng, Zhang Weidi, Wu Yi, Qu Chuyuan, Hu Hongru, Lee Teresa, Lin Siyu, Zhang Jiawei, Lam Kit S, Wang Aijun

机构信息

Department of Biomedical Engineering, University of California, Davis, Davis, CA 95616, USA.

Center for Surgical Bioengineering, Department of Surgery, University of California, Davis, School of Medicine, Sacramento, CA 95817, USA.

出版信息

STAR Protoc. 2023 May 1;4(2):102259. doi: 10.1016/j.xpro.2023.102259.

Abstract

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..

摘要

传统的抗癌药物筛选方法依赖于化学反应,这种方法既耗时、 labor intensive,又成本高昂。在这里,我们提出了一种使用视觉Transformer和Conv2D进行无标记和高通量药物疗效评估的方案。我们描述了细胞培养、药物处理、数据收集和预处理的步骤。然后,我们详细介绍了深度学习模型的构建及其用于预测药物效力的方法。该方案可适用于筛选影响细胞密度或形态特征的化学物质。有关该方案的使用和执行的完整详细信息,请参考Wang等人的研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d541/10176075/475c88bfa7b5/fx1.jpg

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