| 基于神经网络技术自动筛选Monastrol抑制剂的算法研究 |
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| 引用本文： | 胡艺,章东,张喆,葛云,周晓波.基于神经网络技术自动筛选Monastrol抑制剂的算法研究[J].南京大学学报(自然科学版),2009,45(1). |
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| 作者姓名： | 胡艺 章东 张喆 葛云 周晓波 |
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| 作者单位： | [1]模式识别国家重点实验室,中国科学院自动化研究所,北京 100080 [2]南京大学声学研究所,电子科学与工程系,南京 210093 [3]Harvard Center for Neurodegeneration and Repair-Center for Bioinformatics, Harvard Medical School, 1249 Boylston, Boston, MA 02215, USA） |
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| 基金项目： | 教育部新世纪优秀人才计划项目(06-0450) |
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| 摘 要： | 带自动荧光显微镜的HCS（high content screening）系统是一种新兴的显微摄影、筛选和处理系统.HCS系统在摄影过程中会产生大量数据,人工筛选和识别费时费力.本文为了进行Monastrol抑制剂的筛选,基于前馈式神经网络技术研究了一种对HCS系统摄影的大量细胞同时进行特征提取和细胞显型识别的自动算法.在得到各个通道分离的图像后,对不同通道图像进行并行预处理,并采用神经网络和逻辑运算相结合的算法进行处理.我们将该方法运用于Monastrol抑制剂的筛选中,并将结果与人工识别结果进行分析比较.相比较前人提出的multi-phenotypic mitotic analysis（MMA）算法自动识别的正确率得以提高,可更好评估抗癌药剂效用.
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| 关 键 词： | 高容量分析 高容量筛选 图像分析 神经网络 模式识别 |
Classification approach based on neural network for high content analysis in Monastrol suppressor screens |
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| Hu Yi,Zhang Dong,Zhang Zhe,Ge Yun,Zhou Xiao-Bo.Classification approach based on neural network for high content analysis in Monastrol suppressor screens[J].Journal of Nanjing University: Nat Sci Ed,2009,45(1). |
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| Authors: | Hu Yi Zhang Dong Zhang Zhe Ge Yun Zhou Xiao-Bo |
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| Institution: | 1.National Laboratory of Pattern Recognition;Beijing;100080;China;2.Institute of Acoustics;Nanjing University;Nanjing;210093;3.Harvard Center for Neurodegeneration and Repair-Center for Bioinformatics;Harvard Medical School;1249 Boylston;Boston;MA 02215;USA |
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| Abstract: | HCS(high content screening) viaautomated fluorescent microscopy is a powerful technology for its effective expression of cellular processes. This technique obtains specific phenotypic information of cellular images by employing selected fluorescence probes. However, it generates great amount of image datasets at one time, which leads to difficulty in handling and analyzing. Manual recognition makes the processing extremely time-consuming and subjective. Consequently, there is an urgent need to develop effec... |
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| Keywords: | high content analysis high content screening image analysis back propagation neural network pattern recognition |
| 本文献已被 CNKI 维普 等数据库收录！ |
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