Identifying functional evolution processes of cancer according to regression residuals network

Bolin Cheny; Manting Yang; Xuequn Shang

doi:10.1109/BIBM52615.2021.9669862

Identifying functional evolution processes of cancer according to regression residuals network

Bolin Cheny
, Manting Yang
, Xuequn Shang

School of Computer Science

Northwestern Polytechnical University Xian

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

1 Scopus citations

Abstract

The occurrence and development of cancer is a complex biological process, which usually requires further research by constructing a network. The existing methods of constructing networks are usually based on similarity to construct co-expression networks for analysis, but this similarity-based method usually ignores the data whose expression level increases or decreases at the same time in different groups. Gene pairs may also be related to the evolution of cancer, and that may cause part of the information to be lost. Therefore, we propose a new method of constructing a network based on regression residuals. Then, the module is identified based on the regression residual network according to different stages of cancer. A module network between cancer stages is obtained through the similarity between the modules. Finally, based on the size and density of the subnets in the modular network, enjoy the screening, and finally selected 3 subnets for functional analysis and Veen diagram analysis, and found some biological functions and genes that have been confirmed to be related to cancer.

Original language	English
Title of host publication	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Editors	Yufei Huang, Lukasz Kurgan, Feng Luo, Xiaohua Tony Hu, Yidong Chen, Edward Dougherty, Andrzej Kloczkowski, Yaohang Li
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3713-3719
Number of pages	7
ISBN (Electronic)	9781665401265
DOIs	https://doi.org/10.1109/BIBM52615.2021.9669862
State	Published - 2021
Event	2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 - Virtual, Online, United States Duration: 9 Dec 2021 → 12 Dec 2021

Publication series

Name	Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Conference

Conference	2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Country/Territory	United States
City	Virtual, Online
Period	9/12/21 → 12/12/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

biological functions
co-expression networks
module network
regression residual

Access to Document

10.1109/BIBM52615.2021.9669862

Cite this

Cheny, B., Yang, M., & Shang, X. (2021). Identifying functional evolution processes of cancer according to regression residuals network. In Y. Huang, L. Kurgan, F. Luo, X. T. Hu, Y. Chen, E. Dougherty, A. Kloczkowski, & Y. Li (Eds.), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 (pp. 3713-3719). (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM52615.2021.9669862

Cheny, Bolin ; Yang, Manting ; Shang, Xuequn. / Identifying functional evolution processes of cancer according to regression residuals network. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. editor / Yufei Huang ; Lukasz Kurgan ; Feng Luo ; Xiaohua Tony Hu ; Yidong Chen ; Edward Dougherty ; Andrzej Kloczkowski ; Yaohang Li. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 3713-3719 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

@inproceedings{4b102dacf6554f079edcdadd172f1d7d,

title = "Identifying functional evolution processes of cancer according to regression residuals network",

abstract = "The occurrence and development of cancer is a complex biological process, which usually requires further research by constructing a network. The existing methods of constructing networks are usually based on similarity to construct co-expression networks for analysis, but this similarity-based method usually ignores the data whose expression level increases or decreases at the same time in different groups. Gene pairs may also be related to the evolution of cancer, and that may cause part of the information to be lost. Therefore, we propose a new method of constructing a network based on regression residuals. Then, the module is identified based on the regression residual network according to different stages of cancer. A module network between cancer stages is obtained through the similarity between the modules. Finally, based on the size and density of the subnets in the modular network, enjoy the screening, and finally selected 3 subnets for functional analysis and Veen diagram analysis, and found some biological functions and genes that have been confirmed to be related to cancer.",

keywords = "biological functions, co-expression networks, module network, regression residual",

author = "Bolin Cheny and Manting Yang and Xuequn Shang",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021 ; Conference date: 09-12-2021 Through 12-12-2021",

year = "2021",

doi = "10.1109/BIBM52615.2021.9669862",

language = "英语",

series = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3713--3719",

editor = "Yufei Huang and Lukasz Kurgan and Feng Luo and Hu, \{Xiaohua Tony\} and Yidong Chen and Edward Dougherty and Andrzej Kloczkowski and Yaohang Li",

booktitle = "Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021",

}

Cheny, B, Yang, M & Shang, X 2021, Identifying functional evolution processes of cancer according to regression residuals network. in Y Huang, L Kurgan, F Luo, XT Hu, Y Chen, E Dougherty, A Kloczkowski & Y Li (eds), Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Institute of Electrical and Electronics Engineers Inc., pp. 3713-3719, 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, Virtual, Online, United States, 9/12/21. https://doi.org/10.1109/BIBM52615.2021.9669862

Identifying functional evolution processes of cancer according to regression residuals network. / Cheny, Bolin; Yang, Manting; Shang, Xuequn.
Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. ed. / Yufei Huang; Lukasz Kurgan; Feng Luo; Xiaohua Tony Hu; Yidong Chen; Edward Dougherty; Andrzej Kloczkowski; Yaohang Li. Institute of Electrical and Electronics Engineers Inc., 2021. p. 3713-3719 (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021).

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

TY - GEN

T1 - Identifying functional evolution processes of cancer according to regression residuals network

AU - Cheny, Bolin

AU - Yang, Manting

AU - Shang, Xuequn

PY - 2021

Y1 - 2021

N2 - The occurrence and development of cancer is a complex biological process, which usually requires further research by constructing a network. The existing methods of constructing networks are usually based on similarity to construct co-expression networks for analysis, but this similarity-based method usually ignores the data whose expression level increases or decreases at the same time in different groups. Gene pairs may also be related to the evolution of cancer, and that may cause part of the information to be lost. Therefore, we propose a new method of constructing a network based on regression residuals. Then, the module is identified based on the regression residual network according to different stages of cancer. A module network between cancer stages is obtained through the similarity between the modules. Finally, based on the size and density of the subnets in the modular network, enjoy the screening, and finally selected 3 subnets for functional analysis and Veen diagram analysis, and found some biological functions and genes that have been confirmed to be related to cancer.

AB - The occurrence and development of cancer is a complex biological process, which usually requires further research by constructing a network. The existing methods of constructing networks are usually based on similarity to construct co-expression networks for analysis, but this similarity-based method usually ignores the data whose expression level increases or decreases at the same time in different groups. Gene pairs may also be related to the evolution of cancer, and that may cause part of the information to be lost. Therefore, we propose a new method of constructing a network based on regression residuals. Then, the module is identified based on the regression residual network according to different stages of cancer. A module network between cancer stages is obtained through the similarity between the modules. Finally, based on the size and density of the subnets in the modular network, enjoy the screening, and finally selected 3 subnets for functional analysis and Veen diagram analysis, and found some biological functions and genes that have been confirmed to be related to cancer.

KW - biological functions

KW - co-expression networks

KW - module network

KW - regression residual

UR - https://www.scopus.com/pages/publications/85125206875

U2 - 10.1109/BIBM52615.2021.9669862

DO - 10.1109/BIBM52615.2021.9669862

M3 - 会议稿件

AN - SCOPUS:85125206875

T3 - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

SP - 3713

EP - 3719

BT - Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

A2 - Huang, Yufei

A2 - Kurgan, Lukasz

A2 - Luo, Feng

A2 - Hu, Xiaohua Tony

A2 - Chen, Yidong

A2 - Dougherty, Edward

A2 - Kloczkowski, Andrzej

A2 - Li, Yaohang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

Y2 - 9 December 2021 through 12 December 2021

ER -

Cheny B, Yang M, Shang X. Identifying functional evolution processes of cancer according to regression residuals network. In Huang Y, Kurgan L, Luo F, Hu XT, Chen Y, Dougherty E, Kloczkowski A, Li Y, editors, Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 3713-3719. (Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021). doi: 10.1109/BIBM52615.2021.9669862

Identifying functional evolution processes of cancer according to regression residuals network

Abstract

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UN SDGs

Keywords

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