title: A support vector machine classifier for recognizing mitotic subphases using high-content screening data.
abstract: High-content screening studies of mitotic checkpoints are important for identifying cancer targets and developing novel cancer-specific therapies. A crucial step in such a study is to determine the stage of cell cycle. Due to the overwhelming number of cells assayed in a high-content screening experiment and the multiple factors that need to be taken into consideration for accurate determination of mitotic subphases, an automated classifier is necessary. In this article, the authors describe in detail a support vector machine (SVM) classifier that they have implemented to recognize various mitotic subphases. In contrast to previous studies to recognize subcellular patterns, they used only low-resolution cell images and a few parameters that can be calculated inexpensively with off-the-shelf image-processing software. The performance of the SVM was evaluated with a cross-validation method and was shown to be comparable to that of a human expert.
authors: Genome and Proteome Sciences Novartis Institutes for Biomedical Research 250 Massachusetts Avenue Cambridge, MA 02139, USA. charles.tao@novartis.com
authors: Charles Y
authors: CY
authors: Tao
authors: Jonathan
authors: J
authors: Hoyt
authors: Yan
authors: Y
authors: Feng
mesh_terms: D003582:Cytogenetics / Q000295:instrumentation*; D006367:HeLa Cells; D006801:Humans; D008938:Mitosis / Q000502:physiology*