Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence.

MLA

Washburn, Jacob D., et al. “Evolutionarily Informed Deep Learning Methods for Predicting Relative Transcript Abundance from DNA Sequence.” Proceedings of the National Academy of Sciences of the United States of America, vol. 116, no. 12, Mar. 2019, pp. 5542–49. EBSCOhost, https://doi.org/10.1073/pnas.1814551116.

APA

Washburn, J. D., Mejia-Guerra, M. K., Ramstein, G., Kremling, K. A., Valluru, R., Buckler, E. S., & Wang, H. (2019). Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence. Proceedings of the National Academy of Sciences of the United States of America, 116(12), 5542–5549. https://doi.org/10.1073/pnas.1814551116

Chicago

Washburn, Jacob D, Maria Katherine Mejia-Guerra, Guillaume Ramstein, Karl A Kremling, Ravi Valluru, Edward S Buckler, and Hai Wang. 2019. “Evolutionarily Informed Deep Learning Methods for Predicting Relative Transcript Abundance from DNA Sequence.” Proceedings of the National Academy of Sciences of the United States of America 116 (12): 5542–49. doi:10.1073/pnas.1814551116.