3.8 Proceedings Paper

HCS-PhenoCluster - Machine Learning based High-Content Phenotypic Analysis Tool

出版社

ASSOC COMPUTING MACHINERY
DOI: 10.1145/3194480.3194505

关键词

High-content screening; Machine learning; Phenotype discovery; Unsupervised clustering; Web-based software

向作者/读者索取更多资源

Computational analysis of multi-parametric high-content imaging dataset can be particularly tedious and daunting to implement for a laboratory biologist with no bioinformatics expertise and hence, there is an unmet need of software tools to facilitate this type of analysis. We present a web-based application HCS-PhenoCluster for the analysis of high-content image-based data which can be used for discovering novel cellular phenotypes beyond visual inspection. This application is interfaced with a single-cell extraction pipeline which performs cell segmentation and multi-feature extraction of high-content imaging data. HCS-PhenoCluster has been implemented in Swift language using Vapor framework and consists of a MySQL database. The image analysis workflow of HCS-PhenoCluster is based on machine learning models implemented in Python and comprises five modules of data processing which include multi-level quality control modules and an unsupervised clustering module to reveal phenotypic diversity in the dataset. We present a case study of the application of this tool for detecting multiple Golgi organizational states. The user manual including the description of methodology and relevant references are available with the application.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

3.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据