Li, L.; Li, L.; Wang, Y.; Wu, B.; Guan, Y.; Chen, Y.; Zhao, J.
Integration of Machine Learning and Experimental Validation to Identify Anoikis-Related Prognostic Signature for Predicting the Breast Cancer Tumor Microenvironment and Treatment Response. Genes 2024, 15, 1458.
https://doi.org/10.3390/genes15111458
AMA Style
Li L, Li L, Wang Y, Wu B, Guan Y, Chen Y, Zhao J.
Integration of Machine Learning and Experimental Validation to Identify Anoikis-Related Prognostic Signature for Predicting the Breast Cancer Tumor Microenvironment and Treatment Response. Genes. 2024; 15(11):1458.
https://doi.org/10.3390/genes15111458
Chicago/Turabian Style
Li, Longpeng, Longhui Li, Yaxin Wang, Baoai Wu, Yue Guan, Yinghua Chen, and Jinfeng Zhao.
2024. "Integration of Machine Learning and Experimental Validation to Identify Anoikis-Related Prognostic Signature for Predicting the Breast Cancer Tumor Microenvironment and Treatment Response" Genes 15, no. 11: 1458.
https://doi.org/10.3390/genes15111458
APA Style
Li, L., Li, L., Wang, Y., Wu, B., Guan, Y., Chen, Y., & Zhao, J.
(2024). Integration of Machine Learning and Experimental Validation to Identify Anoikis-Related Prognostic Signature for Predicting the Breast Cancer Tumor Microenvironment and Treatment Response. Genes, 15(11), 1458.
https://doi.org/10.3390/genes15111458