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Using the Fisher kernel method to detect remote protein homologies (1999)
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| Venue: | In Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology |
| Citations: | 208 - 4 self |
BibTeX
@INPROCEEDINGS{Jaakkola99usingthe,
author = {Tommi Jaakkola and Mark Diekhans and David Haussler},
title = {Using the Fisher kernel method to detect remote protein homologies},
booktitle = {In Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology},
year = {1999},
pages = {149--158},
publisher = {AAAI Press}
}
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Abstract
A new method, called the Fisher kernel method, for detecting remote protein homologies is introduced and shown to perform well in classifying protein domains by SCOP superfamily. The method is a variant of support vector machines using a new kernel function. The kernel function is derived from a hidden Markov model. The general approach of combining generative models like HMMs with discriminative methods such as support vector machines may have applications in other areas of biosequence analysis as well.
