Detection of protein conformation defects from fluorescence microscopy images

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@Article{Guo:2013:EAAI,
  author =       "Peifang Guo and Prabir Bhattacharya",
  title =        "Detection of protein conformation defects from
                 fluorescence microscopy images",
  journal =      "Engineering Applications of Artificial Intelligence",
  volume =       "26",
  number =       "8",
  pages =        "1936--1941",
  year =         "2013",
  ISSN =         "0952-1976",
  DOI =          "doi:10.1016/j.engappai.2013.05.007",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0952197613000948",
  keywords =     "genetic algorithms, genetic programming, EM, Pattern
                 classification, Computer-aided diagnosis, Protein
                 conformational diseases, Histogram, Microscopic images,
                 Texture analysis",
  abstract =     "A diagnostic method for protein conformational
                 diseases (PCD) from microscopy images is proposed when
                 such conformational conflicts involve muscular
                 intra-nuclear inclusions (INIs) indicative of
                 oculopharyngeal muscular dystrophy (OPMD), one variety
                 of PCD. The method combines two techniques: (1) the
                 Histogram Region of Interest Fixed by Thresholds
                 (HRIFT) is designed to capture the colour information
                 of INIs for basic feature extraction; (2) an automated
                 feature synthesis, based on the HRIFT features, is
                 designed to identify OPMD by means of Genetic
                 Programming and the Expectation Maximisation algorithm
                 (GP-EM) for classification improvement. With variations
                 in size, shape, and background structure, a total of
                 600 microscopic images are analysed for the binary
                 classes of healthy and sick conditions of OPMD. The
                 integrated technique of the approach reveals a
                 sensitivity of 0.9 and an area of 0.961 under the
                 receiver operating characteristic (ROC) at a
                 specificity of 0.95. Furthermore, significant
                 improvements in classification accuracy and
                 computational time are demonstrated by comparison with
                 other methods.",
}

Genetic Programming entries for Pei Fang Guo Prabir Bhattacharya

Citations