Source: Scientific Reports. Unidade: ESALQ
Subjects: ANOPHELES, ANTAGONISTAS, APRENDIZADO COMPUTACIONAL, COMPOSTOS VOLÁTEIS, DROSOPHILA, MOSQUITOS, OLFATO, RECEPTORES SENSORIAIS
ABNT
KEPCHIA, Devin et al. Use of machine learning to identify novel, behaviorally active antagonists of the insect odorant receptor co-receptor (Orco) subunit. Scientific Reports, v. 9, p. 1-18, 2019Tradução . . Disponível em: https://doi.org/10.1038/s41598-019-40640-4. Acesso em: 06 out. 2024.APA
Kepchia, D., Xu, P., Terryn, R., Castro, A., Schürer, S. C., Leal, W. S., & Luetje, C. W. (2019). Use of machine learning to identify novel, behaviorally active antagonists of the insect odorant receptor co-receptor (Orco) subunit. Scientific Reports, 9, 1-18. doi:10.1038/s41598-019-40640-4NLM
Kepchia D, Xu P, Terryn R, Castro A, Schürer SC, Leal WS, Luetje CW. Use of machine learning to identify novel, behaviorally active antagonists of the insect odorant receptor co-receptor (Orco) subunit [Internet]. Scientific Reports. 2019 ; 9 1-18.[citado 2024 out. 06 ] Available from: https://doi.org/10.1038/s41598-019-40640-4Vancouver
Kepchia D, Xu P, Terryn R, Castro A, Schürer SC, Leal WS, Luetje CW. Use of machine learning to identify novel, behaviorally active antagonists of the insect odorant receptor co-receptor (Orco) subunit [Internet]. Scientific Reports. 2019 ; 9 1-18.[citado 2024 out. 06 ] Available from: https://doi.org/10.1038/s41598-019-40640-4