Applying ComBat harmonization to ultrasomics enhances machine learning generalizability and performance in multi-center clinical settings.
- DOI
- 10.1016/j.isci.2026.116114
- Published
- 2026 Oct 16
- Container
- iScience
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1016/j.isci.2026.116114,
title = {Applying ComBat harmonization to ultrasomics enhances machine learning generalizability and performance in multi-center clinical settings.},
author = {Lin S and Cai M and Jiang N and Liu H and Liang S and Ye F and Lin X and Wang W and Xu E and Hu H},
year = {2026},
journal = {iScience},
doi = {10.1016/j.isci.2026.116114},
url = {https://doi.org/10.1016/j.isci.2026.116114}
}RIS
TY - JOUR TI - Applying ComBat harmonization to ultrasomics enhances machine learning generalizability and performance in multi-center clinical settings. AU - Lin S AU - Cai M AU - Jiang N AU - Liu H AU - Liang S AU - Ye F AU - Lin X AU - Wang W AU - Xu E AU - Hu H PY - 2026 JO - iScience DO - 10.1016/j.isci.2026.116114 UR - https://doi.org/10.1016/j.isci.2026.116114 ER -
APA
S, L., M, C., N, J., H, L., S, L., F, Y., X, L., W, W., E, X., & H, H. (2026). Applying ComBat harmonization to ultrasomics enhances machine learning generalizability and performance in multi-center clinical settings.. iScience. https://doi.org/10.1016/j.isci.2026.116114
Source records
- pubmed · retrieved 2026-09-26T12:30:02.516Z