Graph kernel-based learning for gene function prediction from gene interaction network

Xin Li; Zhu Zhang; Hsinchun Chen; Jiexun Li

doi:10.1109/BIBM.2007.25

Graph kernel-based learning for gene function prediction from gene interaction network

Xin Li, Zhu Zhang, Hsinchun Chen, Jiexun Li

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

7 Scopus citations

Abstract

Prediction of gene functions is a major challenge to biologists in the post-genomic era. Interactions between genes and their products compose networks and can be used to infer gene functions. Most previous studies used heuristic approaches based on either local or global information of gene interaction networks to assign unknown gene functions. In this study, we propose a graph kernel-based method that can capture the structure of gene interaction networks to predict gene functions. We conducted an experimental study on a test-bed of P53-related genes. The experimental results demonstrated better performance for our proposed method as compared with baseline methods.

Original language	English (US)
Title of host publication	Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007
Pages	368-373
Number of pages	6
DOIs	https://doi.org/10.1109/BIBM.2007.25
State	Published - 2007
Event	2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007 - Fremont, CA, United States Duration: Nov 2 2007 → Nov 4 2007

Publication series

Name	Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007

Other

Other	2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007
Country/Territory	United States
City	Fremont, CA
Period	11/2/07 → 11/4/07

ASJC Scopus subject areas

Biotechnology
General Computer Science
Biomedical Engineering

Access to Document

10.1109/BIBM.2007.25

Cite this

Li, X., Zhang, Z., Chen, H., & Li, J. (2007). Graph kernel-based learning for gene function prediction from gene interaction network. In Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007 (pp. 368-373). Article 4413079 (Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007). https://doi.org/10.1109/BIBM.2007.25

Graph kernel-based learning for gene function prediction from gene interaction network. / Li, Xin; Zhang, Zhu; Chen, Hsinchun et al.
Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007. 2007. p. 368-373 4413079 (Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007).

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

Li, X, Zhang, Z, Chen, H & Li, J 2007, Graph kernel-based learning for gene function prediction from gene interaction network. in Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007., 4413079, Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007, pp. 368-373, 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007, Fremont, CA, United States, 11/2/07. https://doi.org/10.1109/BIBM.2007.25

@inproceedings{9dddfeb7f4e544428e497a40b80b00ff,

title = "Graph kernel-based learning for gene function prediction from gene interaction network",

abstract = "Prediction of gene functions is a major challenge to biologists in the post-genomic era. Interactions between genes and their products compose networks and can be used to infer gene functions. Most previous studies used heuristic approaches based on either local or global information of gene interaction networks to assign unknown gene functions. In this study, we propose a graph kernel-based method that can capture the structure of gene interaction networks to predict gene functions. We conducted an experimental study on a test-bed of P53-related genes. The experimental results demonstrated better performance for our proposed method as compared with baseline methods.",

author = "Xin Li and Zhu Zhang and Hsinchun Chen and Jiexun Li",

year = "2007",

doi = "10.1109/BIBM.2007.25",

language = "English (US)",

isbn = "0769530311",

series = "Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007",

pages = "368--373",

booktitle = "Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007",

note = "2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007 ; Conference date: 02-11-2007 Through 04-11-2007",

}

TY - GEN

T1 - Graph kernel-based learning for gene function prediction from gene interaction network

AU - Li, Xin

AU - Zhang, Zhu

AU - Chen, Hsinchun

AU - Li, Jiexun

PY - 2007

Y1 - 2007

N2 - Prediction of gene functions is a major challenge to biologists in the post-genomic era. Interactions between genes and their products compose networks and can be used to infer gene functions. Most previous studies used heuristic approaches based on either local or global information of gene interaction networks to assign unknown gene functions. In this study, we propose a graph kernel-based method that can capture the structure of gene interaction networks to predict gene functions. We conducted an experimental study on a test-bed of P53-related genes. The experimental results demonstrated better performance for our proposed method as compared with baseline methods.

AB - Prediction of gene functions is a major challenge to biologists in the post-genomic era. Interactions between genes and their products compose networks and can be used to infer gene functions. Most previous studies used heuristic approaches based on either local or global information of gene interaction networks to assign unknown gene functions. In this study, we propose a graph kernel-based method that can capture the structure of gene interaction networks to predict gene functions. We conducted an experimental study on a test-bed of P53-related genes. The experimental results demonstrated better performance for our proposed method as compared with baseline methods.

UR - http://www.scopus.com/inward/record.url?scp=49049098266&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=49049098266&partnerID=8YFLogxK

U2 - 10.1109/BIBM.2007.25

DO - 10.1109/BIBM.2007.25

M3 - Conference contribution

AN - SCOPUS:49049098266

SN - 0769530311

SN - 9780769530314

T3 - Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007

SP - 368

EP - 373

BT - Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007

T2 - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007

Y2 - 2 November 2007 through 4 November 2007

ER -

Graph kernel-based learning for gene function prediction from gene interaction network

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this