NDLI: Discriminating transmembrane proteins from signal peptides using SVM-Fisher approach

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Discriminating transmembrane proteins from signal peptides using SVM-Fisher approach

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2004 International Conference on Machine Learning and Applications, 2004. Proceedings.

Discriminating transmembrane proteins from signal peptides using SVM-Fisher approach

Content Provider	IEEE Xplore Digital Library
Author	Kahsay, R.Y. Gao, G.R. Li Liao
Copyright Year	2005
Description	Author affiliation: Delaware Biotechnol. Inst., Delaware Univ., Newark, DE, USA (Kahsay, R.Y.; Gao, G.R.)
Abstract	Most computational methods for transmembrane protein topology prediction rely on compositional bias of amino acids to locate those hydrophobic domains in transmembrane proteins. Because signal peptides also contain hydrophobic segments, these computational prediction methods often misidentify signal peptides as transmembrane proteins. Here, we present a new approach that combines the SVM-Fisher discrimination method and TMMOD - a hidden Markov model based predictor for transmembrane proteins. While TMMOD alone has already outperformed most existing methods in both identification and topology prediction, this new approach further improves the ability of TMMOD to discriminate between transmembrane proteins and signal peptide containing proteins, reducing mis-prediction of signal peptides by more than 30% in our test data.
File Size	215284
File Format	PDF
ISBN	0769524958
DOI	10.1109/ICMLA.2005.24
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-12-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Biotechnology Sequences Peptides Hidden Markov models Signal processing Amino acids Biomembranes Biochemistry Topology
Content Type	Text
Resource Type	Article

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