Protein secondary structure prediction based on statistical dictionaries

Wei Yang; Kuan Quan Wang; Wang Meng Zuo

doi:10.1109/ICBBE.2009.5163256

Protein secondary structure prediction based on statistical dictionaries

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: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This study first gives the definitions and construction methods of statistical dictionary and similar list, and then proposes two novel algorithms, Hybrid Windows Prediction (HWP) and Similar Searching Prediction (SSP), for protein secondary structure prediction. Our methods not only implement the incremental learning to utilize the increasing protein structure data, but also achieve high prediction accuracies of overall Q3 and SOV by the blind test on the dataset composed of 2825 protein chains.

Original language	English
Title of host publication	3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
DOIs	https://doi.org/10.1109/ICBBE.2009.5163256
State	Published - 2009
Externally published	Yes
Event	3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009 - Beijing, China Duration: 11 Jun 2009 → 13 Jun 2009

Publication series

Name	3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009

Conference

Conference	3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009
Country/Territory	China
City	Beijing
Period	11/06/09 → 13/06/09

Keywords

Classification
Protein structure prediction
Statistical method

Access to Document

10.1109/ICBBE.2009.5163256

Cite this

@inproceedings{df45f8cc1063483a9162b058ef5e2f87,

title = "Protein secondary structure prediction based on statistical dictionaries",

abstract = "This study first gives the definitions and construction methods of statistical dictionary and similar list, and then proposes two novel algorithms, Hybrid Windows Prediction (HWP) and Similar Searching Prediction (SSP), for protein secondary structure prediction. Our methods not only implement the incremental learning to utilize the increasing protein structure data, but also achieve high prediction accuracies of overall Q3 and SOV by the blind test on the dataset composed of 2825 protein chains.",

keywords = "Classification, Protein structure prediction, Statistical method",

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

year = "2009",

doi = "10.1109/ICBBE.2009.5163256",

language = "英语",

isbn = "9781424429028",

series = "3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009",

booktitle = "3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009",

note = "3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009 ; Conference date: 11-06-2009 Through 13-06-2009",

}

Yang, W, Wang, KQ & Zuo, WM 2009, Protein secondary structure prediction based on statistical dictionaries. in 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009., 5163256, 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009, 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009, Beijing, China, 11/06/09. https://doi.org/10.1109/ICBBE.2009.5163256

Protein secondary structure prediction based on statistical dictionaries. / Yang, Wei; Wang, Kuan Quan ; Zuo, Wang Meng.
3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009. 2009. 5163256 (3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Protein secondary structure prediction based on statistical dictionaries

AU - Yang, Wei

AU - Wang, Kuan Quan

AU - Zuo, Wang Meng

PY - 2009

Y1 - 2009

N2 - This study first gives the definitions and construction methods of statistical dictionary and similar list, and then proposes two novel algorithms, Hybrid Windows Prediction (HWP) and Similar Searching Prediction (SSP), for protein secondary structure prediction. Our methods not only implement the incremental learning to utilize the increasing protein structure data, but also achieve high prediction accuracies of overall Q3 and SOV by the blind test on the dataset composed of 2825 protein chains.

AB - This study first gives the definitions and construction methods of statistical dictionary and similar list, and then proposes two novel algorithms, Hybrid Windows Prediction (HWP) and Similar Searching Prediction (SSP), for protein secondary structure prediction. Our methods not only implement the incremental learning to utilize the increasing protein structure data, but also achieve high prediction accuracies of overall Q3 and SOV by the blind test on the dataset composed of 2825 protein chains.

KW - Classification

KW - Protein structure prediction

KW - Statistical method

UR - https://www.scopus.com/pages/publications/72749105434

U2 - 10.1109/ICBBE.2009.5163256

DO - 10.1109/ICBBE.2009.5163256

M3 - 会议稿件

AN - SCOPUS:72749105434

SN - 9781424429028

T3 - 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009

BT - 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009

T2 - 3rd International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2009

Y2 - 11 June 2009 through 13 June 2009

ER -

Protein secondary structure prediction based on statistical dictionaries

Abstract

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Keywords

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