TY - GEN
T1 - Identifying functional evolution processes of cancer according to regression residuals network
AU - Cheny, Bolin
AU - Yang, Manting
AU - Shang, Xuequn
N1 - Publisher Copyright:
© 2021 IEEE.
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 - http://www.scopus.com/inward/record.url?scp=85125206875&partnerID=8YFLogxK
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 -
