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Hidden Markov Models in Molecular Biology: New Algorithms and Applications

Baldi, Pierre and Chauvin, Yves and Hunkapiller, Tim and McClure, Marcella A. (1993) Hidden Markov Models in Molecular Biology: New Algorithms and Applications. In: Advances in Neural Information Processing Systems 5 (NIPS 1992). Advances in Neural Information Processing Systems. No.5. Morgan Kaufmann , San Mateo, CA, pp. 747-754. ISBN 1-55860-274-7.

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Hidden Markov Models (HMMs) can be applied to several important problems in molecular biology. We introduce a new convergent learning algorithm for HMMs that, unlike the classical Baum-Welch algorithm is smooth and can be applied on-line or in batch mode, with or without the usual Viterbi most likely path approximation. Left-right HMMs with insertion and deletion states are then trained to represent several protein families including immunoglobulins and kinases. In all cases, the models derived capture all the important statistical properties of the families and can be used efficiently in a number of important tasks such as multiple alignment, motif detection, and classification.

Item Type:Book Section
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Additional Information:© 1993 Morgan Kaufmann.
Series Name:Advances in Neural Information Processing Systems
Issue or Number:5
Record Number:CaltechAUTHORS:20160129-095602881
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:64088
Deposited By: Kristin Buxton
Deposited On:29 Jan 2016 20:07
Last Modified:03 Oct 2019 09:34

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