Jeremy Huckins

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MOTIVATION Automated identification of cell cycle phases captured via fluorescent microscopy is very important for understanding cell cycle and for drug discovery. In this article, we propose a novel cell detection method that utilizes both the intensity and shape information of the cell for better segmentation quality. In contrast to conventional off-line(More)
Because off-target effects hamper interpretation and validation of RNAi screen data, we developed a bioinformatics method, genome-wide enrichment of seed sequence matches (GESS), to identify candidate off-targeted transcripts in primary screening data. GESS analysis revealed a prominent off-targeted transcript in several screens, including MAD2 (MAD2L1) in(More)
Correct identification of mitosis phase of individual cells in a large population imaged via time-lapse fluorescence microscopy is important for drug discovery and cell cycle study. The large amount of image data makes manually analysis unrealistic, which calls for automatic systems for mitosis cell identification. The automatic system has to be able to(More)
BACKGROUND Automated time-lapse microscopy can visualize proliferation of large numbers of individual cells, enabling accurate measurement of the frequency of cell division and the duration of interphase and mitosis. However, extraction of quantitative information by manual inspection of time-lapse movies is too time-consuming to be useful for analysis of(More)
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