Peer-reviewedCC BY-NC-ND 4.0BMC Medicine
Biologically informed network-regularized sparse consensus clustering (BioNet-SPACC) identifies a continuous plasma proteomic axis in major depressive disorder
Related
- Urinary DNA Methylation Profiling Identifies a Biologically Distinct Subgroup of Low-Grade Urothelial Carcinoma with High-Grade-Associated Features
- Latent class analysis identifies three biologically distinct phenotypes among patients undergoing endovascular thrombectomy: a derivation and temporal validation study
- Machine learning clustering of multidimensional clinical features identifies four prognostically distinct sepsis subtypes
- Distinct insulin sensitivity profiles revealed by clustering based on continuous glucose monitoring data in people living with type 1 diabetes
- Multimodal data-driven clustering analysis of heart failure patients identifies two distinct patterns of heart failure with preserved ejection fraction: Results from the PACIFIC-PRESERVED study
BibTeX
@article{pan2026biologically,
title = {{Biologically informed network-regularized sparse consensus clustering (BioNet-SPACC) identifies a continuous plasma proteomic axis in major depressive disorder}},
author = {Hongxin Pan and Gang Luo and Xiaobing Zhai and Wei Xu and Kefeng Li},
journal = {BMC Medicine},
year = {2026},
month = oct,
doi = {10.1186/s12916-026-05344-3},
url = {https://link.springer.com/article/10.1186/s12916-026-05344-3},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Biologically informed network-regularized sparse consensus clustering (BioNet-SPACC) identifies a continuous plasma proteomic axis in major depressive disorder
AU - Hongxin Pan
AU - Gang Luo
AU - Xiaobing Zhai
AU - Wei Xu
AU - Kefeng Li
PY - 2026
DA - 2026/10/19
JO - BMC Medicine
DO - 10.1186/s12916-026-05344-3
UR - https://link.springer.com/article/10.1186/s12916-026-05344-3
SN - 1741-7015
ER -