Identification of a blood-based 12-gene signature that predicts the severity of coronary artery stenosis: An integrative approach based on gene network construction, Support Vector Machine algorithm, and multi-cohort validation.

MLA

Wang, Xue-Bin, et al. “Identification of a Blood-Based 12-Gene Signature That Predicts the Severity of Coronary Artery Stenosis: An Integrative Approach Based on Gene Network Construction, Support Vector Machine Algorithm, and Multi-Cohort Validation.” Atherosclerosis, vol. 291, Dec. 2019, pp. 34–43. EBSCOhost, https://doi.org/10.1016/j.atherosclerosis.2019.10.001.

APA

Wang, X.-B., Cui, N.-H., Liu, X., & Ming, L. (2019). Identification of a blood-based 12-gene signature that predicts the severity of coronary artery stenosis: An integrative approach based on gene network construction, Support Vector Machine algorithm, and multi-cohort validation. Atherosclerosis, 291, 34–43. https://doi.org/10.1016/j.atherosclerosis.2019.10.001

Chicago

Wang, Xue-Bin, Ning-Hua Cui, Xia’nan Liu, and Liang Ming. 2019. “Identification of a Blood-Based 12-Gene Signature That Predicts the Severity of Coronary Artery Stenosis: An Integrative Approach Based on Gene Network Construction, Support Vector Machine Algorithm, and Multi-Cohort Validation.” Atherosclerosis 291 (December): 34–43. doi:10.1016/j.atherosclerosis.2019.10.001.