Cite
PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework.
MLA
Song, Jiangning, et al. “PREvaIL, an Integrative Approach for Inferring Catalytic Residues Using Sequence, Structural, and Network Features in a Machine-Learning Framework.” Journal of Theoretical Biology, vol. 443, Apr. 2018, pp. 125–37. EBSCOhost, https://doi.org/10.1016/j.jtbi.2018.01.023.
APA
Song, J., Li, F., Takemoto, K., Haffari, G., Akutsu, T., Chou, K.-C., & Webb, G. I. (2018). PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework. Journal of Theoretical Biology, 443, 125–137. https://doi.org/10.1016/j.jtbi.2018.01.023
Chicago
Song, Jiangning, Fuyi Li, Kazuhiro Takemoto, Gholamreza Haffari, Tatsuya Akutsu, Kuo-Chen Chou, and Geoffrey I. Webb. 2018. “PREvaIL, an Integrative Approach for Inferring Catalytic Residues Using Sequence, Structural, and Network Features in a Machine-Learning Framework.” Journal of Theoretical Biology 443 (April): 125–37. doi:10.1016/j.jtbi.2018.01.023.