Cite
Ekins S, Freundlich JS, Clark AM, et al. Machine learning models identify molecules active against the Ebola virus in vitro. F1000Res. 2015;4:1091doi: 10.12688/f1000research.7217.2.
Ekins, S., Freundlich, J. S., Clark, A. M., Anantpadma, M., Davey, R. A., & Madrid, P. (2015). Machine learning models identify molecules active against the Ebola virus in vitro. F1000Research, 41091. https://doi.org/10.12688/f1000research.7217.2
Ekins, Sean, et al. "Machine learning models identify molecules active against the Ebola virus in vitro." F1000Research vol. 4 (2015): 1091. doi: https://doi.org/10.12688/f1000research.7217.2
Ekins S, Freundlich JS, Clark AM, Anantpadma M, Davey RA, Madrid P. Machine learning models identify molecules active against the Ebola virus in vitro. F1000Res. 2015 Oct 20;4:1091. doi: 10.12688/f1000research.7217.2. eCollection 2015. PMID: 26834994; PMCID: PMC4706063.
Copy
Download .nbib