Influence of Encoding Scheme on Protein Secondary Structure Prediction

被引：0

作者：

Zou, Dongsheng ^{[1
]}

He, Zhongshi ^{[1
]}

He, Jingyuan ^{[1
]}

Huang, Xiaofeng ^{[1
]}

机构：

[1] Chongqing Univ, Coll Comp Sci, Chongqing 400044, Peoples R China

来源：

2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23 | 2008年

关键词：

SVM; data encoding; protein secondary structure prediction; Profile encoding;

D O I：

10.1109/WCICA.2008.4593133

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Secondary structure prediction of proteins has increasingly been a central research area in bioinformatics. In order to know which data encoding approach is more effective whiling predicting secondary structure using SVM, five approaches: ENCOrth, ENCFive, ENCCodBas, ENCCodExt and ENCProf are discussed in this paper. The results of data encoding are used as input of SVM. By performing ENCProf approach, the accuracy of Q3 can be improved 19.4%similar to 23.9% more than the other four approaches.

引用

页码：1439 / 1443

页数：5

共 50 条

[31] Use of tetrapeptide signals for protein secondary-structure prediction [J].

Feng, Yonge ;

Luo, Liaofu .

AMINO ACIDS, 2008, 35 (03) :607-614

[32] Use of tetrapeptide signals for protein secondary-structure prediction [J].

Yonge Feng ;

Liaofu Luo .

Amino Acids, 2008, 35 :607-614

[33] Cascading Discriminant and Generative Models for Protein Secondary Structure Prediction [J].

Thomarat, Fabienne ;

Lauer, Fabien ;

Guermeur, Yann .

PATTERN RECOGNITION IN BIOINFORMATICS, 2012, 7632 :166-177

[34] Deep metric learning for accurate protein secondary structure prediction [J].

Yang, Wei ;

Liu, Yang ;

Xiao, Chunjing .

KNOWLEDGE-BASED SYSTEMS, 2022, 242

[35] Protein Secondary Structure Prediction using Large Margin Methods [J].

Tang, Buzhou ;

Wang, Xuan ;

Wang, Xiaolong .

PROCEEDINGS OF THE 8TH IEEE/ACIS INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE, 2009, :142-146

[36] Sequence/structure similarity and support vector machine for protein secondary structure prediction [J].

Lin, JH ;

Tsai, CL ;

Lin, MR .

8TH WORLD MULTI-CONFERENCE ON SYSTEMICS, CYBERNETICS AND INFORMATICS, VOL XIII, PROCEEDINGS: INDUSTRIAL SYSTEMS, 2004, :71-76

[37] Association classification algorithm based on structure sequence in protein secondary structure prediction [J].

Zhou, Zhun ;

Yang, Bingru ;

Hou, Wei .

EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (09) :6381-6389

[38] Prediction of Protein Secondary Structure using SVM-PSSM Classifier Combined by Sequence Features [J].

Chen, Yehong ;

Liu, Yihui ;

Cheng, Jinyong ;

Wang, Yanchun .

PROCEEDINGS OF 2016 IEEE ADVANCED INFORMATION MANAGEMENT, COMMUNICATES, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IMCEC 2016), 2016, :103-106

[39] A Novel Approach of Protein Secondary Structure Prediction by SVM Using PSSM Combined by Sequence Features [J].

Chen, Yehong ;

Cheng, Jinyong ;

Liu, Yihui ;

Park, Pil Seong .

PROCEEDINGS OF SAI INTELLIGENT SYSTEMS CONFERENCE (INTELLISYS) 2016, VOL 1, 2018, 15 :1074-1084

[40] A new hybrid coding for protein secondary structure prediction based on primary structure similarity [J].

Li, Zhong ;

Wang, Jing ;

Zhang, Shunpu ;

Zhang, Qifeng ;

Wu, Wuming .

GENE, 2017, 618 :8-13

← 1 2 3 4 5 →