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Bioimage Informatics - Automated Subcellular Location Determination

    July 29, 2009 - Greg.

    Required Reading:

    1. M. V. Boland and R. F. Murphy (2001). A Neural Network Classifier Capable of Recognizing the Patterns of all Major Subcellular Structures in Fluorescence Microscope Images of HeLa Cells. Bioinformatics 17:1213-1223. PDF Link

    • One of the first papers to apply machine learning to automated image analysis.

    2. Thouis R. Jones et al. (2009). Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning Proceedings of the National Academy of Sciences 106, no. 6: 1826-1831. PDF Link

    • The state of the art in image analysis.

    3. J. Newberg, J. Hua, and R.F. Murphy (2009). Location Proteomics: Systematic Determination of Protein Subcellular Location. Methods in Molecular Biology, Systems Biology, volume 500, I. Maly (ed.), Humana Press, pp. 313-332. PDF Link

    • General overview of automated bioimage analysis.

    Further Reading:

    Reviews:

    • E. Glory and R.F. Murphy (2007). Automated Subcellular Location Determination and High Throughput Microscopy. Developmental Cell 12:7-16. PDF Link
    • H. Peng (2008). Bioimage informatics: a new area of engineering biology. Bioinformatics 24:1827-1836. PDF Link
    • Roy Wollman and Nico Stuurman (2007). High throughput microscopy: from raw images to discoveries. J Cell Sci 120, no. 21: 3715-3722.  PDF Link

    Research Articles:

    • Michael D. Slack et al. (2008). Characterizing heterogeneous cellular responses to perturbations. Proceedings of the National Academy of Sciences 105, no. 49: 19306-19311. PDF Link
    • Marko Tscherepanow, Nickels Jensen, and Franz Kummert. (2008) An incremental approach to automated protein localisation. BMC Bioinformatics 9, no. 1: 445. PDF Link
    • David N. Breslauer et al. (2009) Mobile Phone Based Clinical Microscopy for Global Health Applications. PLoS ONE 4, no. 7: e6320.  Link
    • Using a mobile phone with attached microscope to take a picture of a cell culture and then use automatic image analysis to count the number of tuberculosis bacteria present.
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