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ICASSP
2009
IEEE

Conditional random fields for the prediction of signal peptide cleavage sites

10 years 4 months ago
Conditional random fields for the prediction of signal peptide cleavage sites
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.
Man-Wai Mak, Sun-Yuan Kung
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where ICASSP
Authors Man-Wai Mak, Sun-Yuan Kung
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