Cite
Machine Learning Enables High-Throughput Phenotyping for Analyses of the Genetic Architecture of Bulliform Cell Patterning in Maize
MLA
Pengfei Qiao, et al. “Machine Learning Enables High-Throughput Phenotyping for Analyses of the Genetic Architecture of Bulliform Cell Patterning in Maize.” G3: Genes, Genomes, Genetics, vol. 9, no. 12, Dec. 2019, pp. 4235–43. EBSCOhost, https://doi.org/10.1534/g3.119.400757.
APA
Pengfei Qiao, Meng Lin, Miguel Vasquez, Susanne Matschi, James Chamness, Matheus Baseggio, Laurie G. Smith, Mert R. Sabuncu, Michael A. Gore, & Michael J. Scanlon. (2019). Machine Learning Enables High-Throughput Phenotyping for Analyses of the Genetic Architecture of Bulliform Cell Patterning in Maize. G3: Genes, Genomes, Genetics, 9(12), 4235–4243. https://doi.org/10.1534/g3.119.400757
Chicago
Pengfei Qiao, Meng Lin, Miguel Vasquez, Susanne Matschi, James Chamness, Matheus Baseggio, Laurie G. Smith, Mert R. Sabuncu, Michael A. Gore, and Michael J. Scanlon. 2019. “Machine Learning Enables High-Throughput Phenotyping for Analyses of the Genetic Architecture of Bulliform Cell Patterning in Maize.” G3: Genes, Genomes, Genetics 9 (12): 4235–43. doi:10.1534/g3.119.400757.