Intelligent Interfaces for Mining Large-Scale RNAi-HCS Image Databases.
Intelligent Interfaces for Mining Large-Scale RNAi-HCS Image Databases.
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用于挖掘大规模 RNAi-HCS 图像数据库的智能接口。
DOI：
10.1109/bibe.2007.4375742
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
Perrimon,Norbert
中科院分区：
文献类型：
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作者：
Lin,Chen;Mak,Wayne;Hong,Pengyu;Sepp,Katharine;Perrimon,Norbert
近年来，高含量筛选(HCS)与RNA干扰(RNAi)相结合，成为通过RNAi诱导的细胞表型分析来研究基因和生物网络的一种重要的基于图像的高通量方法。然而，全基因组的RNAi-HCS屏幕通常会生成数以万计的图像，其中大多数由于现有的HCS图像分析工具的不足而仍未分类。到目前为止，它仍然需要训练有素的科学家浏览一个令人望而却步的大型RNAi-HCS图像数据库，并且只产生关于细胞形态表型的少数定性结果。为此，我们开发了智能接口，以促进HCS技术在生物医学研究中的应用。我们的新界面赋予生物学家计算能力，不仅可以有效和高效地探索大规模RNAi-HCS图像数据库，还可以将他们的知识和经验应用于使用基于内容的图像检索(CBIR)和相关反馈(RF)技术进行细胞表型的交互式挖掘。
Recently, high-content screening (HCS) has been combined with RNA interference (RNAi) to become an essential image-based high-throughput method for studying genes and biological networks through RNAi-induced cellular phenotype analyses. However, a genome-wide RNAi-HCS screen typically generates tens of thousands of images, most of which remain uncategorized due to the inadequacies of existing HCS image analysis tools. Until now, it still requires highly trained scientists to browse a prohibitively large RNAi-HCS image database and produce only a handful of qualitative results regarding cellular morphological phenotypes. For this reason we have developed intelligent interfaces to facilitate the application of the HCS technology in biomedical research. Our new interfaces empower biologists with computational power not only to effectively and efficiently explore large-scale RNAi-HCS image databases, but also to apply their knowledge and experience to interactive mining of cellular phenotypes using Content-Based Image Retrieval (CBIR) with Relevance Feedback (RF) techniques.