Protein secondary structure prediction based on GP-MaxEnt model

Wei Yang; Kuan Quan Wang; Wang Meng Zuo

Protein secondary structure prediction based on GP-MaxEnt model

Wei Yang^*
, Kuan Quan Wang
, Wang Meng Zuo

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology

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

Abstract

Aimed at solving the problem of single-sequence protein secondary structure prediction, a novel method based on Gaussian prior maximum entropy (GP-MaxEnt) model is proposed. In this method, the feature construction was firstly performed based on the conformational preference of amino acid residues, and the improved iterative scaling (IIS) method was used to train the GP-MaxEnt model. CB513 dataset was employed to test this model. The experimental results indicate that the proposed method is effective and can achieve better results in predictive accuracy.

Original language	English
Pages (from-to)	65-68
Number of pages	4
Journal	Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology
Volume	41
Issue number	8
State	Published - Aug 2009
Externally published	Yes

Keywords

Gaussian prior
Maximum entropy model
Protein secondary structure prediction
Single-sequence prediction

Cite this

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title = "Protein secondary structure prediction based on GP-MaxEnt model",

abstract = "Aimed at solving the problem of single-sequence protein secondary structure prediction, a novel method based on Gaussian prior maximum entropy (GP-MaxEnt) model is proposed. In this method, the feature construction was firstly performed based on the conformational preference of amino acid residues, and the improved iterative scaling (IIS) method was used to train the GP-MaxEnt model. CB513 dataset was employed to test this model. The experimental results indicate that the proposed method is effective and can achieve better results in predictive accuracy.",

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author = "Wei Yang and Wang, \{Kuan Quan\} and Zuo, \{Wang Meng\}",

year = "2009",

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T1 - Protein secondary structure prediction based on GP-MaxEnt model

AU - Yang, Wei

AU - Wang, Kuan Quan

AU - Zuo, Wang Meng

PY - 2009/8

Y1 - 2009/8

N2 - Aimed at solving the problem of single-sequence protein secondary structure prediction, a novel method based on Gaussian prior maximum entropy (GP-MaxEnt) model is proposed. In this method, the feature construction was firstly performed based on the conformational preference of amino acid residues, and the improved iterative scaling (IIS) method was used to train the GP-MaxEnt model. CB513 dataset was employed to test this model. The experimental results indicate that the proposed method is effective and can achieve better results in predictive accuracy.

AB - Aimed at solving the problem of single-sequence protein secondary structure prediction, a novel method based on Gaussian prior maximum entropy (GP-MaxEnt) model is proposed. In this method, the feature construction was firstly performed based on the conformational preference of amino acid residues, and the improved iterative scaling (IIS) method was used to train the GP-MaxEnt model. CB513 dataset was employed to test this model. The experimental results indicate that the proposed method is effective and can achieve better results in predictive accuracy.

KW - Gaussian prior

KW - Maximum entropy model

KW - Protein secondary structure prediction

KW - Single-sequence prediction

UR - https://www.scopus.com/pages/publications/72449207595

M3 - 文章

AN - SCOPUS:72449207595

SN - 0367-6234

VL - 41

SP - 65

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JO - Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology

JF - Harbin Gongye Daxue Xuebao/Journal of Harbin Institute of Technology

IS - 8

ER -

Protein secondary structure prediction based on GP-MaxEnt model

Abstract

Keywords

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