仿形(计算机编程)
计算机科学
图像处理
软件
可扩展性
数据挖掘
原始数据
人工智能
图像(数学)
数据库
程序设计语言
操作系统
作者
Erik Serrano,Srinivas Niranj Chandrasekaran,Dave Bunten,Kenneth I Brewer,Jenna Tomkinson,Roshan Kern,Michael Bornholdt,Stephen Fleming,Ruifan Pei,John Arévalo,Hillary Tsang,Vincent Rubinetti,Callum Tromans‐Coia,Tim Becker,Erin Weisbart,Charlotte Bunne,Alexandr A. Kalinin,Rebecca A. Senft,Stephen J. Taylor,Nasim Jamali
出处
期刊:Cornell University - arXiv
日期:2023-01-01
被引量:7
标识
DOI:10.48550/arxiv.2311.13417
摘要
Advances in high-throughput microscopy have enabled the rapid acquisition of large numbers of high-content microscopy images. Whether by deep learning or classical algorithms, image analysis pipelines then produce single-cell features. To process these single-cells for downstream applications, we present Pycytominer, a user-friendly, open-source python package that implements the bioinformatics steps, known as image-based profiling. We demonstrate Pycytominers usefulness in a machine learning project to predict nuisance compounds that cause undesirable cell injuries.
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