Intelligent Interfaces for Interactive Analysis of High-Content Cellular Images

用于高内容细胞图像交互式分析的智能界面

• 批准号: 7617093
• 负责人: Pengyu Hong
• 金额: $ 17.42万
• 依托单位: BRANDEIS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-13 至 2011-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7617093
• 关键词: Alzheimer' s disease model; Biological; Biomedical Research; Cells; Cellular Morphology; Chemicals; Communities; Computer Simulation; Data; Data Analyses; Databases; Development; Disease model; Drosophila genus; Educational process of instructing; Environment; Feedback; Future; Gene Expression; Genes; Goals; Human; Huntington Disease; Image; Image Analysis; Knowledge; Left; Light; Metadata; Methods; Mining; Modeling; Neurons; Pattern; Phenotype; Preclinical Drug Evaluation; Procedures; Process; RNA Interference; Recording of previous events; Research; Research Personnel; Retrieval; Scheme; Scientist; Screening procedure; System; Techniques; Technology; Testing; Training; User-Computer Interface; Visual; anticancer research; base; cell type; cellular imaging; detector; drug discovery; empowered; experience; gene function; genome-wide; high throughput technology; image processing; innovation; interest; novel; programs; research study; tool

DESCRIPTION (provided by applicant): Cell-based High-Content Screening (HCS) has recently led to high-throughput image-based studies of cellular phenotypes under various external treatments such as chemical compound or or RNA interference (RNAi). Such studies will significantly advance our understanding of gene functions, shed new light on the underlying biological networks, and have direct impact on cancer research and drug discovery/development. However, due to the inadequacies of existing image analysis tools, most HCS screens only relied on analyses of simple marker readouts and left the most informative and profound aspects of cellular morphology unexplored. Domain knowledge is yet to be accumulated for developing image analysis tools to effectively and thoroughly analyze highly diverse cellular images generated by the HCS technology, which are relatively new to image processing research. Nonetheless, building up domain knowledge requires human experts to visually explore a prohibitively large number of images. Therefore, it calls for a new computing paradigm that facilitates teamwork between experimental and computational biologists to overcome this dilemma. We propose to develop a novel computing paradigm that integrates unsupervised pattern mining techniques, visual data exploration interfaces and content-based image retrieval with relevance feedback techniques to facilitate the application of the HCS technology to biomedical research. This paradigm will be realized as a system called imCellPhen, which will be evalutated and tested in the context of two morphological screens of Drosophila neurodisease models using the HCS technology. The main features of imCellPhen are its intelligent interfaces that allow users to (a) effectively and efficiently navigate large-scale HCS image databases, (b) reliably detect novel cellular phenotypes, and (c) teach the system to recognize cellular phenotypes by interactively training computational models. The model training procedure is in fact an implicit, seamless, and effective process for accumulating domain knowledge. The scheme and techniques developed in this research will benefit any HCS screens and thus will be valuable tools for the biomedical research community.

描述（由申请人提供）：基于细胞的高素质筛查（HCS）最近导致了对各种外部处理（例如化学化合物或RNA干扰（RNAI））的细胞表型的高通量图像研究。这样的研究将显着提高我们对基因功能的理解，对潜在的生物网络进行新的启示，并直接影响癌症研究和药物发现/发育。但是，由于现有图像分析工具的不足，大多数HCS筛选仅依赖于简单标记读数的分析，并且没有探索细胞形态的最有用和最深刻的方面。用于开发图像分析工具的域知识尚未积累，以有效地分析由HCS技术产生的高度多样化的细胞图像，HCS技术对图像处理研究是相对较新的。但是，建立领域知识需要人类专家在视觉上探索大量图像。因此，它要求建立一个新的计算范式，以促进实验生物学家和计算生物学家之间的团队合作，以克服这一困境。我们建议开发一种新颖的计算范式，该范式将无监督的模式采矿技术，视觉数据探索接口和基于内容的图像检索与相关反馈技术相结合，以促进HCS技术在生物医学研究中的应用。该范式将被实现为一种称为Imcellphen的系统，将在使用HCS技术的两个果蝇神经疾病模型的形态学筛选中进行评估和测试。 Imcellphen的主要特征是其智能接口，使用户可以（a）有效，有效地导航大型HCS图像数据库，（b）可靠地检测到新颖的细胞表型，（c）教导系统通过交互式训练计算模型来识别蜂窝表型。实际上，模型培训程序是一个隐式，无缝且有效的过程，用于累积领域知识。本研究中开发的计划和技术将使任何HCS屏幕受益，因此将成为生物医学研究界的宝贵工具。

项目成果

Automatic inference of multicellular regulatory networks using informative priors.

使用信息先验自动推断多细胞调控网络。

• DOI: 10.1504/ijcbdd.2009.028820
• 发表时间: 2009
• 期刊: International journal of computational biology and drug design
• 作者: Sun,Xiaoyun;Hong,Pengyu
• 通讯作者: Hong,Pengyu

Oligodendrocyte development and myelinogenesis are not impaired by high concentrations of phenylalanine or its metabolites.

• DOI: 10.1007/s10545-010-9052-3
• 发表时间: 2010-04
• 期刊: JOURNAL OF INHERITED METABOLIC DISEASE
• 影响因子: 4.2
• 作者: Schoemans, Renaud;Aigrot, Marie-Stephane;Wu, Chaohong;Maree, Raphael;Hong, Pengyu;Belachew, Shibeshi;Josse, Claire;Lubetzki, Catherine;Bours, Vincent
• 通讯作者: Bours, Vincent

Intelligent Interfaces for Mining Large-Scale RNAi-HCS Image Databases.

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
• 发表时间: 2007
• 期刊: Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering
• 作者: Lin,Chen;Mak,Wayne;Hong,Pengyu;Sepp,Katharine;Perrimon,Norbert
• 通讯作者: Perrimon,Norbert

Identification of neural outgrowth genes using genome-wide RNAi.

• DOI: 10.1371/journal.pgen.1000111
• 发表时间: 2008-07-04
• 期刊: PLOS GENETICS
• 影响因子: 4.5
• 作者: Sepp, Katharine J.;Hong, Pengyu;Lizarraga, Sofia B.;Liu, Judy S.;Mejia, Luis A.;Walsh, Christopher A.;Perrimon, Norbert
• 通讯作者: Perrimon, Norbert