An interpretable machine learning model for predicting prognosis of medulloblastoma integrating genetic and clinical features.

Su Y, Deng K, Chen X, Feng Z, Wang D, Daniels C, Koh HY, Gonzalez RD, Suzuki H, Miyauchi T, Liu F, Wang W, Li J, Li S, Chen R, Qiu X, Li C, Jiang T, Taylor MD, Zhang J, Liu H, Tian Y

Open source

DOI
10.1038/s43856-026-01454-4
Published
2026 Mar 10
Container
Communications medicine
Publisher
Not recorded
Open access
yes

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BibTeX

@article{allodium:10.1038/s43856-026-01454-4,
  title = {An interpretable machine learning model for predicting prognosis of medulloblastoma integrating genetic and clinical features.},
  author = {Su Y and Deng K and Chen X and Feng Z and Wang D and Daniels C and Koh HY and Gonzalez RD and Suzuki H and Miyauchi T and Liu F and Wang W and Li J and Li S and Chen R and Qiu X and Li C and Jiang T and Taylor MD and Zhang J and Liu H and Tian Y},
  year = {2026},
  journal = {Communications medicine},
  doi = {10.1038/s43856-026-01454-4},
  url = {https://doi.org/10.1038/s43856-026-01454-4}
}

RIS

TY  - JOUR
TI  - An interpretable machine learning model for predicting prognosis of medulloblastoma integrating genetic and clinical features.
AU  - Su Y
AU  - Deng K
AU  - Chen X
AU  - Feng Z
AU  - Wang D
AU  - Daniels C
AU  - Koh HY
AU  - Gonzalez RD
AU  - Suzuki H
AU  - Miyauchi T
AU  - Liu F
AU  - Wang W
AU  - Li J
AU  - Li S
AU  - Chen R
AU  - Qiu X
AU  - Li C
AU  - Jiang T
AU  - Taylor MD
AU  - Zhang J
AU  - Liu H
AU  - Tian Y
PY  - 2026
JO  - Communications medicine
DO  - 10.1038/s43856-026-01454-4
UR  - https://doi.org/10.1038/s43856-026-01454-4
ER  - 

APA

Y, S., K, D., X, C., Z, F., D, W., C, D., HY, K., RD, G., H, S., T, M., F, L., W, W., J, L., S, L., R, C., X, Q., C, L., T, J., MD, T., J, Z., H, L., & Y, T. (2026). An interpretable machine learning model for predicting prognosis of medulloblastoma integrating genetic and clinical features.. Communications medicine. https://doi.org/10.1038/s43856-026-01454-4

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