Hidden Markov models for chromosome identification

Presents a hidden Markov model for automatic karyotyping. Previously, we demonstrated that this method is robust in the presence of different types of metaphase spreads, truncation of chromosomes and minor chromosome abnormalities, and that it gives results superior to neural networks on standard da...

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Main Authors: Conroy, J.M., Becker, R.L., Lefkowitz, W., Christopher, K.L., Surana, R.B., O'Leary, T., O'Leary, D.P., Kolda, T.G.
Format: Conference Proceeding
Language:English
Subjects:
Amniotic fluid
Biological cells
Biological neural networks
Blood
Bones
Hidden Markov models
Military computing
Neural networks
Pathology
Robustness
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creator Conroy, J.M.
Becker, R.L.
Lefkowitz, W.
Christopher, K.L.
Surana, R.B.
O'Leary, T.
O'Leary, D.P.
Kolda, T.G.
description Presents a hidden Markov model for automatic karyotyping. Previously, we demonstrated that this method is robust in the presence of different types of metaphase spreads, truncation of chromosomes and minor chromosome abnormalities, and that it gives results superior to neural networks on standard data sets. In this paper, we evaluate it on a data set consisting of a mix of chromosomes obtained from blood, amniotic fluid and bone marrow specimens. The method is shown to be robust on this mixed set of data, as well as giving far superior results than those obtained by neural networks.
doi_str_mv 10.1109/CBMS.2001.941764
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Amniotic fluid
Biological cells
Biological neural networks
Blood
Bones
Hidden Markov models
Military computing
Neural networks
Pathology
Robustness
title Hidden Markov models for chromosome identification
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