NDLI: A new approach for HMM based protein sequence family modeling and its application to remote homology classification

Content Provider	IEEE Xplore Digital Library
Author	Plotz, T. Fink, G.A.
Copyright Year	2002
Description	Author affiliation: Fac. of Technol., Bielefeld Univ. (Plotz, T.; Fink, G.A.)
Abstract	Currently probabilistic models of protein families, namely HMMs, are the methodology of choice for remote homology analysis. Unfortunately, the topology of such so-called Profile HMMs is rather complex which, despite sophisticated regularization techniques, is problematic for robust model estimation when only little training data is available. We propose a new HMM based protein family modeling method using building blocks which capture the essentials of particular targets only. They are estimated in a fully data-driven and unsupervised procedure. Contrary to current motif detection procedures we use a feature based protein sequence representation we developed earlier. Such small building blocks are automatically combined to global protein family HMMs which can be applied to remote homology analysis tasks. The results of an experimental evaluation on a challenging task of remote homology classification prove that robust models containing substantially smaller amounts of parameters can be estimated using the new modeling approach. The smaller the number of parameters to be trained, the smaller the number of training samples required which is of major importance for e.g. drug discovery tasks
Starting Page	1008
Ending Page	1013
File Size	182293
Page Count	6
File Format	PDF
ISBN	0780394038
DOI	10.1109/SSP.2005.1628742
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-17
Publisher Place	Russia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Protein sequence Robustness Biological information theory Drugs Sequences Automatic speech recognition Topology Training data Biological system modeling
Content Type	Text
Resource Type	Article

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