El-Awadi, R.; Gomez, O.D.; Castillo-Secilla, D.; Torres, C.; Herrera, L.J.; Rojas, I.; Ortuño, F.M.
Interrelational Proteomic Sequence Features Enhance Predictive Modeling: Application to COVID-19 Severity. Biomedicines 2026, 14, 378.
https://doi.org/10.3390/biomedicines14020378
AMA Style
El-Awadi R, Gomez OD, Castillo-Secilla D, Torres C, Herrera LJ, Rojas I, Ortuño FM.
Interrelational Proteomic Sequence Features Enhance Predictive Modeling: Application to COVID-19 Severity. Biomedicines. 2026; 14(2):378.
https://doi.org/10.3390/biomedicines14020378
Chicago/Turabian Style
El-Awadi, Radwa, Oscar D. Gomez, Daniel Castillo-Secilla, Carolina Torres, Luis J. Herrera, Ignacio Rojas, and Francisco M. Ortuño.
2026. "Interrelational Proteomic Sequence Features Enhance Predictive Modeling: Application to COVID-19 Severity" Biomedicines 14, no. 2: 378.
https://doi.org/10.3390/biomedicines14020378
APA Style
El-Awadi, R., Gomez, O. D., Castillo-Secilla, D., Torres, C., Herrera, L. J., Rojas, I., & Ortuño, F. M.
(2026). Interrelational Proteomic Sequence Features Enhance Predictive Modeling: Application to COVID-19 Severity. Biomedicines, 14(2), 378.
https://doi.org/10.3390/biomedicines14020378