Conditional random fields for the prediction of signal peptide cleavage sites

Man Wai Mak, Sun Yuan Kung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)

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.
Original languageEnglish
Title of host publication2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
Pages1605-1608
Number of pages4
DOIs
Publication statusPublished - 23 Sep 2009
Event2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan
Duration: 19 Apr 200924 Apr 2009

Conference

Conference2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
Country/TerritoryTaiwan
CityTaipei
Period19/04/0924/04/09

Keywords

  • Cleavage sites
  • Conditional random fields
  • Discriminative models
  • Protein sequences
  • Signal peptides

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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