Classifying six glioma subtypes from combined gene expression and CNVs data based on compressive sensing approach

Wenlong Tang; Hongbao Cao; Ji Gang Zhang; Junbo Duan; Dongdong Lin; Yu Ping Wang

doi:10.1109/BIBMW.2011.6112388

Classifying six glioma subtypes from combined gene expression and CNVs data based on compressive sensing approach

Wenlong Tang, Hongbao Cao, Ji Gang Zhang, Junbo Duan, Dongdong Lin, Yu Ping Wang

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

Abstract

It is realized that a combined analysis of different types of genomic measurements tends to give more reliable classification results. However, how to efficiently combine data with different resolutions is challenging. We propose a novel compressed sensing based approach for the combined analysis of gene expression and copy number variants data for the purpose of subtyping six types of Gliomas. Experiment results show that the proposed combined approach can substantially improve the classification accuracy compared to that of using either of individual data type. The proposed approach can be applicable to many other types of genomic data.

Original language	English
Title of host publication	2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011
Pages	282-288
Number of pages	7
DOIs	https://doi.org/10.1109/BIBMW.2011.6112388
State	Published - 2011
Externally published	Yes
Event	2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011 - Atlanta, GA, United States Duration: Nov 12 2011 → Nov 15 2011

Publication series

Name	2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

Conference

Conference	2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011
Country/Territory	United States
City	Atlanta, GA
Period	11/12/11 → 11/15/11

Keywords

Combined
compressive sensing
glioma
subtyping

Access to Document

10.1109/BIBMW.2011.6112388

Cite this

Tang, W., Cao, H., Zhang, J. G., Duan, J., Lin, D., & Wang, Y. P. (2011). Classifying six glioma subtypes from combined gene expression and CNVs data based on compressive sensing approach. In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011 (pp. 282-288). Article 6112388 (2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011). https://doi.org/10.1109/BIBMW.2011.6112388

Tang, Wenlong ; Cao, Hongbao ; Zhang, Ji Gang et al. / Classifying six glioma subtypes from combined gene expression and CNVs data based on compressive sensing approach. 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011. 2011. pp. 282-288 (2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011).

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title = "Classifying six glioma subtypes from combined gene expression and CNVs data based on compressive sensing approach",

abstract = "It is realized that a combined analysis of different types of genomic measurements tends to give more reliable classification results. However, how to efficiently combine data with different resolutions is challenging. We propose a novel compressed sensing based approach for the combined analysis of gene expression and copy number variants data for the purpose of subtyping six types of Gliomas. Experiment results show that the proposed combined approach can substantially improve the classification accuracy compared to that of using either of individual data type. The proposed approach can be applicable to many other types of genomic data.",

keywords = "Combined, compressive sensing, glioma, subtyping",

author = "Wenlong Tang and Hongbao Cao and Zhang, {Ji Gang} and Junbo Duan and Dongdong Lin and Wang, {Yu Ping}",

year = "2011",

doi = "10.1109/BIBMW.2011.6112388",

language = "Ingl{\'e}s",

isbn = "9781457716133",

series = "2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011",

pages = "282--288",

booktitle = "2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011",

note = "2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011 ; Conference date: 12-11-2011 Through 15-11-2011",

}

Tang, W, Cao, H, Zhang, JG, Duan, J, Lin, D & Wang, YP 2011, Classifying six glioma subtypes from combined gene expression and CNVs data based on compressive sensing approach. in 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011., 6112388, 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011, pp. 282-288, 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011, Atlanta, GA, United States, 11/12/11. https://doi.org/10.1109/BIBMW.2011.6112388

Classifying six glioma subtypes from combined gene expression and CNVs data based on compressive sensing approach. / Tang, Wenlong; Cao, Hongbao; Zhang, Ji Gang et al.
2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011. 2011. p. 282-288 6112388 (2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011).

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

TY - GEN

T1 - Classifying six glioma subtypes from combined gene expression and CNVs data based on compressive sensing approach

AU - Tang, Wenlong

AU - Cao, Hongbao

AU - Zhang, Ji Gang

AU - Duan, Junbo

AU - Lin, Dongdong

AU - Wang, Yu Ping

PY - 2011

Y1 - 2011

N2 - It is realized that a combined analysis of different types of genomic measurements tends to give more reliable classification results. However, how to efficiently combine data with different resolutions is challenging. We propose a novel compressed sensing based approach for the combined analysis of gene expression and copy number variants data for the purpose of subtyping six types of Gliomas. Experiment results show that the proposed combined approach can substantially improve the classification accuracy compared to that of using either of individual data type. The proposed approach can be applicable to many other types of genomic data.

AB - It is realized that a combined analysis of different types of genomic measurements tends to give more reliable classification results. However, how to efficiently combine data with different resolutions is challenging. We propose a novel compressed sensing based approach for the combined analysis of gene expression and copy number variants data for the purpose of subtyping six types of Gliomas. Experiment results show that the proposed combined approach can substantially improve the classification accuracy compared to that of using either of individual data type. The proposed approach can be applicable to many other types of genomic data.

KW - Combined

KW - compressive sensing

KW - glioma

KW - subtyping

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U2 - 10.1109/BIBMW.2011.6112388

DO - 10.1109/BIBMW.2011.6112388

M3 - Contribución a la conferencia

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T3 - 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

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EP - 288

BT - 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

T2 - 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011

Y2 - 12 November 2011 through 15 November 2011

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Tang W, Cao H, Zhang JG, Duan J, Lin D, Wang YP. Classifying six glioma subtypes from combined gene expression and CNVs data based on compressive sensing approach. In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011. 2011. p. 282-288. 6112388. (2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2011). doi: 10.1109/BIBMW.2011.6112388

Classifying six glioma subtypes from combined gene expression and CNVs data based on compressive sensing approach

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