A local search appproach for transmembrane segment and signal peptide discrimination

EVOLUTIONARY COMPUTATION, MACHINE LEARNING AND DATA MINING IN BIOINFORMATICS, PROCEEDINGS(2010)

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摘要
Discriminating between secreted and membrane proteins is a challenging task. This is particularly true for discriminating between transmembrane segments and signal peptides because they have common biochemical properties. In this paper, we introduce a new predictive method called LSTranslocon (Local Search Translocon) based on a Local Search methodology. The method takes advantage of the latest knowledge in the field to model the biological behaviors of proteins with the aim of ensuring good prediction. The LS Prediction approach is assessed on a constructed data set from Swiss-Prot database and compared with one of the best methods from the literature.
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关键词
swiss-prot database,challenging task,common biochemical property,transmembrane segment,peptide discrimination,best method,good prediction,new predictive method,local search translocon,ls prediction approach,local search methodology,biological behavior,local search appproach,signal peptide,membrane protein,local search,amino acid
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