A Machine Learning-Based algorithm for the assessment of clinical metabolomic fingerprints in Zika virus disease

Authors: Jarrin, Enrique Pelaez, Cordeiro, Fernanda Bertuccez, Medranda, Washington Cardenas, Barrett, Michael, Zambrano, Mildred, Regato, Mary

Source: 2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI) Computational Intelligence (LA-CCI), 2019 IEEE Latin American Conference on. :1-6 Nov, 2019

Relation: 2019 IEEE Latin American Conference on Computational Intelligence (LA-CCI)

Climate services for health: predicting the evolution of the 2016 dengue season in Machala, Ecuador

Authors: Lowe, Rachel, Stewart-Ibarra, Anna M, Petrova, Desislava, García-Díez, Markel, Borbor-Cordova, Mercy J, Mejía, Raúl, Regato, Mary, Rodó, Xavier

Source: In The Lancet Planetary Health July 2017 1(4):e142-e151

Alignment of multiple metabolomics LC-MS datasets from disparate diseases to reveal fever-associated metabolites.

Authors: Năstase, Ana-Maria, Barrett, Michael P., Cárdenas, Washington B., Cordeiro, Fernanda Bertuccez, Zambrano, Mildred, Andrade, Joyce, Chang, Juan, Regato, Mary, Carrillo, Eugenia, Botana, Laura, Moreno, Javier, Regnault, Clément, Milne, Kathryn, Spence, Philip J., Rowe, J. Alexandra, Rogers, Simon

Source: PLoS Neglected Tropical Diseases; 7/24/2023, Vol. 17 Issue 7, p1-22, 22p

Subject Terms: ZIKA virus infections, MIDDLE-income countries, METABOLOMICS, LOW-income countries, SUPERVISED learning

Modeling gene sequence changes over time in type 3 dengue viruses from Ecuador

Authors: Fajardo, Alvaro, Recarey, Ricardo, de Mora, Domenica, D’ Andrea, Lucía, Alvarez, Macarena, Regato, Mary, Colina, Rodney, Khan, Baldip, Cristina, Juan

Source: In Virus Research 2009 141(1):105-109

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