Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18249
Title: Conditional random fields for the prediction of signal peptide cleavage sites
Authors: Mak, MW 
Kung, SY
Keywords: Conditional random fields
Cleavage sites
Discriminative models
Protein sequences
Signal peptides
Issue Date: 2009
Publisher: IEEE
Source: IEEE International Conference on Acoustics, Speech and Signal Processing, 2009 : ICASSP 2009, 19-24 April 2009, Taipei, p. 1605-1608 How to cite?
Abstract: Correct prediction of signal peptide cleavage sites has a significant impact on drug design. State-of-the-art approaches to cleavage site prediction typically use generative models (such as HMMs) to represent the statistics of amino acid sequences or use neural networks to detect the changes in short amino-acid segments along a query sequence. By formulating cleavage site prediction as a sequence labeling problem, this paper demonstrates how conditional random fields (CRFs) can be applied to cleavage site prediction. The paper also demonstrates how amino acid properties can be exploited and incorporated into the CRFs to boost prediction performance. Results show that the performance of CRFs is comparable to that of a state-of-the-art predictor (SignalP V3.0). Further performance improvement was observed when the decisions of SignalP and the CRF-based predictor are fused.
URI: http://hdl.handle.net/10397/18249
ISBN: 978-1-4244-2353-8
978-1-4244-2354-5 (E-ISBN)
ISSN: 1520-6149
DOI: 10.1109/ICASSP.2009.4959906
Appears in Collections:Conference Paper

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