A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD.

MLA

Zhao, Zhenyu, et al. “A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD.” Frontiers in Immunology, vol. 13, May 2022, p. 872387. EBSCOhost, https://doi.org/10.3389/fimmu.2022.872387.

APA

Zhao, Z., Yin, W., Peng, X., Cai, Q., He, B., Shi, S., Peng, W., Tu, G., Li, Y., Li, D., Tao, Y., Peng, M., Wang, X., & Yu, F. (2022). A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD. Frontiers in Immunology, 13, 872387. https://doi.org/10.3389/fimmu.2022.872387

Chicago

Zhao, Zhenyu, Wei Yin, Xiong Peng, Qidong Cai, Boxue He, Shuai Shi, Weilin Peng, et al. 2022. “A Machine-Learning Approach to Developing a Predictive Signature Based on Transcriptome Profiling of Ground-Glass Opacities for Accurate Classification and Exploring the Immune Microenvironment of Early-Stage LUAD.” Frontiers in Immunology 13 (May): 872387. doi:10.3389/fimmu.2022.872387.