Amino acid composition distribution: A novel sequence representation for prediction of protein subcellular localization

Jianyu Shi; Shaowu Zhang; Quan Pan; Guo Ping Zhou

doi:10.1109/ICBBE.2007.33

Amino acid composition distribution: A novel sequence representation for prediction of protein subcellular localization

Jianyu Shi
, Shaowu Zhang
, Quan Pan
, Guo Ping Zhou

School of Automation

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

2 Scopus citations

Abstract

A novel representation of protein sequence, amino acid composition distribution (AACD), is introduced to perform prediction of subcellular localization in this paper. First, a protein sequence is divided equally into multiple segments. Then, amino acid composition of each segment is calculated in series. After that, each protein sequence can be represented a feature vector. Finally, feature vectors of all sequences are further input into multi-class support vector machines to predict the subcellular localization. The results show that AACD is more effective to represent protein sequence and is non-sensitive to sequence similarity because of the better ability to reflect the information of protein subcellular localization.

Original language	English
Title of host publication	2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE
Pages	115-118
Number of pages	4
DOIs	https://doi.org/10.1109/ICBBE.2007.33
State	Published - 2007
Event	2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE - Wuhan, China Duration: 6 Jul 2007 → 8 Jul 2007

Publication series

Name	2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE

Conference

Conference	2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE
Country/Territory	China
City	Wuhan
Period	6/07/07 → 8/07/07

Keywords

Amino acid composition distribution
Protein subcellular localization
Support vector machines

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10.1109/ICBBE.2007.33

Cite this

Shi, J., Zhang, S., Pan, Q., & Zhou, G. P. (2007). Amino acid composition distribution: A novel sequence representation for prediction of protein subcellular localization. In 2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE (pp. 115-118). Article 4272517 (2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE). https://doi.org/10.1109/ICBBE.2007.33

@inproceedings{d333d606e83d461f9e7535e44fddaef3,

title = "Amino acid composition distribution: A novel sequence representation for prediction of protein subcellular localization",

abstract = "A novel representation of protein sequence, amino acid composition distribution (AACD), is introduced to perform prediction of subcellular localization in this paper. First, a protein sequence is divided equally into multiple segments. Then, amino acid composition of each segment is calculated in series. After that, each protein sequence can be represented a feature vector. Finally, feature vectors of all sequences are further input into multi-class support vector machines to predict the subcellular localization. The results show that AACD is more effective to represent protein sequence and is non-sensitive to sequence similarity because of the better ability to reflect the information of protein subcellular localization.",

keywords = "Amino acid composition distribution, Protein subcellular localization, Support vector machines",

author = "Jianyu Shi and Shaowu Zhang and Quan Pan and Zhou, \{Guo Ping\}",

year = "2007",

doi = "10.1109/ICBBE.2007.33",

language = "英语",

isbn = "1424411203",

series = "2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE",

pages = "115--118",

booktitle = "2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE",

note = "2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE ; Conference date: 06-07-2007 Through 08-07-2007",

}

Shi, J, Zhang, S, Pan, Q & Zhou, GP 2007, Amino acid composition distribution: A novel sequence representation for prediction of protein subcellular localization. in 2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE., 4272517, 2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE, pp. 115-118, 2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE, Wuhan, China, 6/07/07. https://doi.org/10.1109/ICBBE.2007.33

Amino acid composition distribution: A novel sequence representation for prediction of protein subcellular localization. / Shi, Jianyu; Zhang, Shaowu; Pan, Quan et al.
2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE. 2007. p. 115-118 4272517 (2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE).

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

TY - GEN

T1 - Amino acid composition distribution

T2 - 2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE

AU - Shi, Jianyu

AU - Zhang, Shaowu

AU - Pan, Quan

AU - Zhou, Guo Ping

PY - 2007

Y1 - 2007

N2 - A novel representation of protein sequence, amino acid composition distribution (AACD), is introduced to perform prediction of subcellular localization in this paper. First, a protein sequence is divided equally into multiple segments. Then, amino acid composition of each segment is calculated in series. After that, each protein sequence can be represented a feature vector. Finally, feature vectors of all sequences are further input into multi-class support vector machines to predict the subcellular localization. The results show that AACD is more effective to represent protein sequence and is non-sensitive to sequence similarity because of the better ability to reflect the information of protein subcellular localization.

AB - A novel representation of protein sequence, amino acid composition distribution (AACD), is introduced to perform prediction of subcellular localization in this paper. First, a protein sequence is divided equally into multiple segments. Then, amino acid composition of each segment is calculated in series. After that, each protein sequence can be represented a feature vector. Finally, feature vectors of all sequences are further input into multi-class support vector machines to predict the subcellular localization. The results show that AACD is more effective to represent protein sequence and is non-sensitive to sequence similarity because of the better ability to reflect the information of protein subcellular localization.

KW - Amino acid composition distribution

KW - Protein subcellular localization

KW - Support vector machines

UR - https://www.scopus.com/pages/publications/35348860865

U2 - 10.1109/ICBBE.2007.33

DO - 10.1109/ICBBE.2007.33

M3 - 会议稿件

AN - SCOPUS:35348860865

SN - 1424411203

SN - 9781424411207

T3 - 2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE

SP - 115

EP - 118

BT - 2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE

Y2 - 6 July 2007 through 8 July 2007

ER -

Shi J, Zhang S, Pan Q, Zhou GP. Amino acid composition distribution: A novel sequence representation for prediction of protein subcellular localization. In 2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE. 2007. p. 115-118. 4272517. (2007 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE). doi: 10.1109/ICBBE.2007.33

Amino acid composition distribution: A novel sequence representation for prediction of protein subcellular localization

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