TY - GEN
T1 - Com-DNB
T2 - 15th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2024
AU - Wang, Letian
AU - Zhu, Yanbing
AU - Wan, Xiaohua
AU - Zhang, Yiming
AU - Feng, Shuang
AU - Li, Chang
AU - Zhang, Fa
AU - Hu, Bin
N1 - Publisher Copyright:
© 2024 Copyright is held by the owner/author(s).
PY - 2024/12/16
Y1 - 2024/12/16
N2 - Identifying critical states prior to critical transitions in complex biological processes is essential for disease forecasting and early interventional therapy. Due to the complexity of the underlying mechanisms, the currently proposed methods based on the dynamic network biomarker (DNB) theory cannot be well used to identify the critical state in a complex biological process like aging. In this paper, we propose the com-DNB method based on single-sample DNB community detection, which is capable of identifying adaptively-sized DNB communities and makes improvements in the quality of DNBs. Meanwhile, this paper substantially improves the computational efficiency of the original l-DNB part it contains based on the parallel strategy. Finally, using the gene expression data of PBMCs and classical monocytes from 130 healthy human donors, 61-65 years of age is successfully identified as the critical state of aging in men. Therefore, the com-DNB is able to identify single-sample personalized DNBs more efficiently and accurately, which is a significant advance in the field of critical state identification.
AB - Identifying critical states prior to critical transitions in complex biological processes is essential for disease forecasting and early interventional therapy. Due to the complexity of the underlying mechanisms, the currently proposed methods based on the dynamic network biomarker (DNB) theory cannot be well used to identify the critical state in a complex biological process like aging. In this paper, we propose the com-DNB method based on single-sample DNB community detection, which is capable of identifying adaptively-sized DNB communities and makes improvements in the quality of DNBs. Meanwhile, this paper substantially improves the computational efficiency of the original l-DNB part it contains based on the parallel strategy. Finally, using the gene expression data of PBMCs and classical monocytes from 130 healthy human donors, 61-65 years of age is successfully identified as the critical state of aging in men. Therefore, the com-DNB is able to identify single-sample personalized DNBs more efficiently and accurately, which is a significant advance in the field of critical state identification.
KW - aging
KW - community
KW - complex biological process
KW - critical state
KW - dynamic network biomarker (DNB)
UR - http://www.scopus.com/inward/record.url?scp=85216418951&partnerID=8YFLogxK
U2 - 10.1145/3698587.3701338
DO - 10.1145/3698587.3701338
M3 - Conference contribution
AN - SCOPUS:85216418951
T3 - ACM-BCB 2024 - 15th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
BT - ACM-BCB 2024 - 15th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
PB - Association for Computing Machinery, Inc
Y2 - 22 November 2024 through 25 November 2024
ER -
