White JG et al. (1996) Cell "Cleavage plane specification in C. elegans: how to divide the spoils."

White JG, Strome S

[Cell, 1996]

Anyone who has watched an early embryo develop cannot help but be awed by the choreography of the early cleavages. The orientation and timing of cleavage in an animal cell are always such that the cleavage furrow bisects the mitotic apparatus (MA) during telophase, thus ensuring the equal partitioning of daughter chromosomes. In addition, the regulation of cleavage plane orientation is necessary for correct partitioning of localized determinants to specific daughter cells, for optimal positioning of cells in developing embryos, and for morphogenesis in plants, which are not motile.

Ruvkun GB et al. (1998) Science "The taxonomy of developmental control in Caenorhabditis elegans."

Ruvkun GB, Hobert O

[Science, 1998]

The Caenorhabditis elegans genome sequence was surveyed for transcription factor and signaling gene families that have been shown to regulate development in a variety of species. About 10 to 25 percent of the genes in most of the gene families already have been genetically analyzed in C. elegans, about half of the genes detect probable orthologs in other species, and about 10 to 25 percent of the genes are, at present, unique to C. elegans. Caenorhabditis elegans is also missing genes that are found in vertebrates and other invertebrates. Thus the genome sequence reveals universals in developmental control that are the legacy of metazoan complexity before the Cambrian explosion, as well as genes that have been more recently invented or lost in particular phylogenetic lineages.AD - Department of Molecular Biology, Massachusetts General Hospital, Department of Genetics, Harvard Medical School, Boston, MA 02114, USA. ruvkun@frodo.mgh.harvard.eduFAU - Ruvkun, GAU - Ruvkun GFAU - Hobert, OAU - Hobert OLA - engPT - Journal ArticlePT - ReviewPT - Review, TutorialCY - UNITED STATESTA - ScienceJID - 0404511RN - 0 (Helminth Proteins)RN - 0 (Transcription Factors)SB - IM

Wang MFZ et al. (2017) Biochim Biophys Acta "(Machine-)learning to analyze in vivo microscopy: Support vector machines."

Wang MFZ, Fernandez-Gonzalez R

[Biochim Biophys Acta, 2017]

The development of new microscopy techniques for super-resolved, long-term monitoring of cellular and subcellular dynamics in living organisms is revealing new fundamental aspects of tissue development and repair. However, new microscopy approaches present several challenges. In addition to unprecedented requirements for data storage, the analysis of high resolution, time-lapse images is too complex to be done manually. Machine learning techniques are ideally suited for the (semi-)automated analysis of multidimensional image data. In particular, support vector machines (SVMs), have emerged as an efficient method to analyze microscopy images obtained from animals. Here, we discuss the use of SVMs to analyze in vivo microscopy data. We introduce the mathematical framework behind SVMs, and we describe the metrics used by SVMs and other machine learning approaches to classify image data. We discuss the influence of different SVM parameters in the context of an algorithm for cell segmentation and tracking. Finally, we describe how the application of SVMs has been critical to study protein localization in yeast screens, for lineage tracing in C. elegans, or to determine the developmental stage of Drosophila embryos to investigate gene expression dynamics. We propose that SVMs will become central tools in the analysis of the complex image data that novel microscopy modalities have made possible. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.