Prediction for signal peptides based on the similarity of global alignment

Hui Liu; Jie Yang; Jun Chen; Dan Qing Liu

Prediction for signal peptides based on the similarity of global alignment

Hui Liu, Jie Yang, Jun Chen, Dan Qing Liu

Quantitative Health Sciences

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

Based on the similarity of global alignment, is was proposed to predict the signal peptides and then avoid the shortcomings of the prior methods by scaling windows, such as loss of useful information and imbalance problem. The similarity can be proved to be embedded into Euclidean space and support vector machine (SVM) can be applied for classification and prediction. The algorithm was carried out on the popular dataset of Neilsen and also compared with other methods. The results prove the stable and high predictive performance.

Original language	English (US)
Pages (from-to)	11-15
Number of pages	5
Journal	Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University
Volume	42
Issue number	1
State	Published - Jan 2008

Keywords

Bioinformatics
Global alignment
Signal peptides prediction
Support vector machine (SVM)

ASJC Scopus subject areas

General

Cite this

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title = "Prediction for signal peptides based on the similarity of global alignment",

abstract = "Based on the similarity of global alignment, is was proposed to predict the signal peptides and then avoid the shortcomings of the prior methods by scaling windows, such as loss of useful information and imbalance problem. The similarity can be proved to be embedded into Euclidean space and support vector machine (SVM) can be applied for classification and prediction. The algorithm was carried out on the popular dataset of Neilsen and also compared with other methods. The results prove the stable and high predictive performance.",

keywords = "Bioinformatics, Global alignment, Signal peptides prediction, Support vector machine (SVM)",

author = "Hui Liu and Jie Yang and Jun Chen and Liu, {Dan Qing}",

year = "2008",

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AU - Liu, Hui

AU - Yang, Jie

AU - Chen, Jun

AU - Liu, Dan Qing

PY - 2008/1

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N2 - Based on the similarity of global alignment, is was proposed to predict the signal peptides and then avoid the shortcomings of the prior methods by scaling windows, such as loss of useful information and imbalance problem. The similarity can be proved to be embedded into Euclidean space and support vector machine (SVM) can be applied for classification and prediction. The algorithm was carried out on the popular dataset of Neilsen and also compared with other methods. The results prove the stable and high predictive performance.

AB - Based on the similarity of global alignment, is was proposed to predict the signal peptides and then avoid the shortcomings of the prior methods by scaling windows, such as loss of useful information and imbalance problem. The similarity can be proved to be embedded into Euclidean space and support vector machine (SVM) can be applied for classification and prediction. The algorithm was carried out on the popular dataset of Neilsen and also compared with other methods. The results prove the stable and high predictive performance.

KW - Bioinformatics

KW - Global alignment

KW - Signal peptides prediction

KW - Support vector machine (SVM)

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JF - Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University

IS - 1

ER -

Prediction for signal peptides based on the similarity of global alignment

Abstract

Keywords

ASJC Scopus subject areas

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