Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) S.r.l., IRCCS, Via Piero Maroncelli 40, 47014 Meldola (FC), Italy.
Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesvári körút 62, 6726 Szeged, Hungary.
Cell Syst. 2017 Jun 28;4(6):651-655.e5. doi: 10.1016/j.cels.2017.05.012. Epub 2017 Jun 21.
High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org.
高内涵、基于成像的筛选现在通常会生成规模大到无法手动验证和查询的数据集。应用机器学习的软件已成为自动化分析的重要工具，但这些方法需要经过注释的示例来学习。有效地探索大型数据集以找到相关示例仍然是一个具有挑战性的瓶颈。在这里，我们介绍高级细胞分类器 (ACC)，这是一个用于表型分析的图形软件包，可解决这些困难。ACC 将机器学习和图像分析方法应用于基于细胞的大规模实验生成的高内涵数据。它具有挖掘微观图像数据、发现新表型和提高识别性能的方法。我们证明这些功能大大加快了训练过程，成功地发现了罕见的表型，并提高了分析的准确性。ACC 有广泛的文档记录，旨在为没有机器学习专业知识的研究人员提供用户友好的界面，并作为免费的开源工具在 www.cellclassifier.org 上发布。
