Abstract
Multi-tag bioimaging systems such as the toponome imaging system (TIS) require sophisticated analytical methods to extract molecular signatures of various types of cells. In this paper, we present a novel paradigm for mining cell phenotypes based on their high-dimensional co-expression profiles contained within the images generated by the robotically controlled TIS microscope installed at Warwick. The proposed paradigm employs a refined cell segmentation algorithm followed by a locality preserving nonlinear embedding algorithm which is shown to produce significantly better cell classification and phenotype distribution results as compared to its linear counterpart.
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Khan, A.M., Humayun, A., Raza, SeA., Khan, M., Rajpoot, N.M. (2012). A Novel Paradigm for Mining Cell Phenotypes in Multi-tag Bioimages Using a Locality Preserving Nonlinear Embedding. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34478-7_70
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DOI: https://doi.org/10.1007/978-3-642-34478-7_70
Publisher Name: Springer, Berlin, Heidelberg
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