Bayesian fluorescence in situ hybridisation signal classification

Previous research has indicated the significance of accurate classification of fluorescence in situ hybridisation (FISH) signals for the detection of genetic abnormalities. Based on well-discriminating features and a trainable neural network (NN) classifier, a previous system enabled highly-accurate classification of valid signals and artefacts of two fluorophores. However, since this system employed several features that are considered independent, the naive Bayesian classifier (NBC) is suggested here as an alternative to the NN. The NBC independence assumption permits the decomposition of the high-dimensional likelihood of the model for the data into a product of one-dimensional probability densities. The naive independence assumption together with the Bayesian methodology allow the NBC to predict a posteriori probabilities of class membership using estimated classconditional densities in a close and simple form. Since the probability densities are the only parameters of the NBC, th...

• Type: Journal
• Year: 2004
• Where: ARTMED
• Authors: Boaz Lerner
• Keywords: ARTMED 2004, Densities, Probability Densities, Spherical Covariance Matrices