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Chromosome Identification Using Hidden Markov Models



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0023 6837 00 8011 1629 03 00 0 LABORATORY INVESTIGATION Copyright 2000 by The United States and Canadian Academy of Pathology Inc Vol 80 No 11 p 1629 2000 Printed in U S A Chromosome Identification Using Hidden Markov Models Comparison with Neural Networks Singular Value Decomposition Principal Components Analysis and Fisher Discriminant Analysis John M Conroy Tamara G Kolda Dianne P O Leary and Timothy J O Leary Center for Computing Sciences JMC Institute for Defense Analyses Bowie Maryland and Computational Sciences and Mathematics Research Department TGK Sandia National Laboratories Livermore California and Computer Science Department and Institute for Advanced Computer Studies DPO University of Maryland College Park Maryland and Department of Cellular Pathology TJO Armed Forces Institute of Pathology Washington DC SUMMARY The analysis of G banded chromosomes remains the most important tool available to the clinical cytogeneticist The analysis is laborious when performed manually and the utility of automated chromosome identification algorithms has been limited by the fact that classification accuracy of these methods seldom exceeds about 80 in routine practice In this study we use four new approaches to automated chromosome identification singular value decomposition SVD principal components analysis PCA Fisher discriminant analysis FDA and hidden Markov models HMM to classify three well known chromosome data sets Philadelphia Edinburgh and Copenhagen comparing these approaches with the use of neural networks NN We show that the HMM is a particularly robust approach to identification that attains classification accuracies of up to 97 for normal chromosomes and retains classification accuracies of up to 95 when chromosome telomeres are truncated or small portions of the chromosome are inverted This represents a substantial improvement of the classification accuracy for normal chromosomes and a doubling in classification accuracy for truncated chromosomes and



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