Applying ComBat harmonization to ultrasomics enhances machine learning generalizability and performance in multi-center clinical settings.

Lin S, Cai M, Jiang N, Liu H, Liang S, Ye F, Lin X, Wang W, Xu E, Hu H

Open source

DOI
10.1016/j.isci.2026.116114
Published
2026 Oct 16
Container
iScience
Publisher
Not recorded
Open access
yes

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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

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