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Hybrid modeling, HMM/NN architectures, and protein applications

Baldi, Pierre and Chauvin, Yves (1996) Hybrid modeling, HMM/NN architectures, and protein applications. Neural Computation, 8 (7). pp. 1541-1565. ISSN 0899-7667. doi:10.1162/neco.1996.8.7.1541.

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We describe a hybrid modeling approach where the parameters of a model are calculated and modulated by another model, typically a neural network (NN), to avoid both overfitting and underfitting. We develop the approach for the case of Hidden Markov Models (HMMs), by deriving a class of hybrid HMM/NN architectures. These architectures can be trained with unified algorithms that blend HMM dynamic programming with NN backpropagation. In the case of complex data, mixtures of HMMs or modulated HMMs must be used. NNs can then be applied both to the parameters of each single HMM, and to the switching or modulation of the models, as a function of input or context. Hybrid HMM/NN architectures provide a flexible NN parameterization for the control of model structure and complexity. At the same time, they can capture distributions that, in practice, are inaccessible to single HMMs. The HMM/NN hybrid approach is tested, in its simplest form, by constructing a model of the immunoglobulin protein family. A hybrid model is trained, and a multiple alignment derived, with less than a fourth of the number of parameters used with previous single HMMs.

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Additional Information:© 1996 The MIT Press. Received February 2, 1995, accepted February 8, 1996. Posted Online April 4, 2008. The work of P.B. is supported by a grant from the ONR. The work of Y.C. is supported in part by Grant R43 LM05780 from the National Library of Medicine. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the National Library of Medicine.
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Office of Naval ResearchUNSPECIFIED
National Library of MedicineR43 LM05780
Issue or Number:7
Record Number:CaltechAUTHORS:BALnc96
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:13275
Deposited By: Marie Noren
Deposited On:11 Feb 2009 04:49
Last Modified:08 Nov 2021 22:36

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