Ribone, A.I.; Fass, M.; Gonzalez, S.; Lia, V.; Paniego, N.; Rivarola, M.
Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance. Plants 2023, 12, 2767.
https://doi.org/10.3390/plants12152767
AMA Style
Ribone AI, Fass M, Gonzalez S, Lia V, Paniego N, Rivarola M.
Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance. Plants. 2023; 12(15):2767.
https://doi.org/10.3390/plants12152767
Chicago/Turabian Style
Ribone, Andrés I., Mónica Fass, Sergio Gonzalez, Veronica Lia, Norma Paniego, and Máximo Rivarola.
2023. "Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance" Plants 12, no. 15: 2767.
https://doi.org/10.3390/plants12152767
APA Style
Ribone, A. I., Fass, M., Gonzalez, S., Lia, V., Paniego, N., & Rivarola, M.
(2023). Co-Expression Networks in Sunflower: Harnessing the Power of Multi-Study Transcriptomic Public Data to Identify and Categorize Candidate Genes for Fungal Resistance. Plants, 12(15), 2767.
https://doi.org/10.3390/plants12152767