Normalization and Selecting Non-Differentially Expressed Genes Improve Machine Learning Modelling of Cross-Platform Transcriptomic Data

Fei Deng, Catherine H. Feng, Nan Gao, Lanjing Zhang

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DOI: 10.53941/tai.2025.100005
Published: 2025-05-26
Container: Transactions on Artificial Intelligence
Publisher: Scilight Press Pty Ltd
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.53941/tai.2025.100005,
  title = {Normalization and Selecting Non-Differentially Expressed Genes Improve Machine Learning Modelling of  Cross-Platform Transcriptomic Data},
  author = {Fei Deng and Catherine H. Feng and Nan Gao and Lanjing Zhang},
  year = {2025},
  journal = {Transactions on Artificial Intelligence},
  doi = {10.53941/tai.2025.100005},
  url = {https://doi.org/10.53941/tai.2025.100005}
}

RIS

TY  - JOUR
TI  - Normalization and Selecting Non-Differentially Expressed Genes Improve Machine Learning Modelling of  Cross-Platform Transcriptomic Data
AU  - Fei Deng
AU  - Catherine H. Feng
AU  - Nan Gao
AU  - Lanjing Zhang
PY  - 2025
JO  - Transactions on Artificial Intelligence
DO  - 10.53941/tai.2025.100005
UR  - https://doi.org/10.53941/tai.2025.100005
ER  -

APA

Deng, F., Feng, C. H., Gao, N., & Zhang, L. (2025). Normalization and Selecting Non-Differentially Expressed Genes Improve Machine Learning Modelling of Cross-Platform Transcriptomic Data. Transactions on Artificial Intelligence. https://doi.org/10.53941/tai.2025.100005

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crossref · retrieved 2026-09-25T21:57:14.798Z