OBJECTIVE: To develop an improved model for the genetic basis of reduced susceptibility to tenofovir in vitro. METHODS: A dataset of 532 HIV-1 subtype B reverse transcriptase genotypes for which matched phenotypic susceptibility data were available was assembled, both as a continuous (transformed) dataset and a categorical dataset generated by imposing a cut-off on the basis of earlier studies of in-vivo response of 1.4-fold. Models were generated using stepwise regression, decision tree and random forest approaches on both the continuous and categorical data. Models were compared by mean squared error (continuous models), or by misclassification rates by nested crossvalidation. RESULTS: From the continuous dataset, stepwise linear regression, regression tree and regression forest methods yielded models with MSE of 0.46, 0.48 and 0.42 respectively. Amino acids 215, 65, 41, 67, 184 and 151 in HIV-1 reverse transcriptase were identified in all three models and amino acid 210 in two. The categorical data yielded logistic regression, classification tree and forest models with misclassification rates of 26, 24 and 23%, respectively. Amino acids 215, 65 and 67 appeared in all; 41, 184, 210 and 151 were also included in the classification forest model. CONCLUSION: The random forests approach has yielded a substantial improvement in the available models to describe the genetic basis of reduced susceptibility to tenofovir in vitro. The most important sites in these models are amino acid sites 215, 65, 41, 67, 184, 151 and 210 in HIV-1 reverse transcriptase.
OBJECTIVE: To develop an improved model for the genetic basis of reduced susceptibility to tenofovir in vitro. METHODS: A dataset of 532 HIV-1 subtype B reverse transcriptase genotypes for which matched phenotypic susceptibility data were available was assembled, both as a continuous (transformed) dataset and a categorical dataset generated by imposing a cut-off on the basis of earlier studies of in-vivo response of 1.4-fold. Models were generated using stepwise regression, decision tree and random forest approaches on both the continuous and categorical data. Models were compared by mean squared error (continuous models), or by misclassification rates by nested crossvalidation. RESULTS: From the continuous dataset, stepwise linear regression, regression tree and regression forest methods yielded models with MSE of 0.46, 0.48 and 0.42 respectively. Amino acids 215, 65, 41, 67, 184 and 151 in HIV-1 reverse transcriptase were identified in all three models and amino acid 210 in two. The categorical data yielded logistic regression, classification tree and forest models with misclassification rates of 26, 24 and 23%, respectively. Amino acids 215, 65 and 67 appeared in all; 41, 184, 210 and 151 were also included in the classification forest model. CONCLUSION: The random forests approach has yielded a substantial improvement in the available models to describe the genetic basis of reduced susceptibility to tenofovir in vitro. The most important sites in these models are amino acid sites 215, 65, 41, 67, 184, 151 and 210 in HIV-1 reverse transcriptase.
Authors: Hassan W Kayondo; Alfred Ssekagiri; Grace Nabakooza; Nicholas Bbosa; Deogratius Ssemwanga; Pontiano Kaleebu; Samuel Mwalili; John M Mango; Andrew J Leigh Brown; Roberto A Saenz; Ronald Galiwango; John M Kitayimbwa Journal: BMC Bioinformatics Date: 2021-11-10 Impact factor: 3.169
Authors: Gilberto Betancor; César Garriga; Maria C Puertas; María Nevot; Lourdes Anta; José L Blanco; M Jesús Pérez-Elías; Carmen de Mendoza; Miguel A Martínez; Javier Martinez-Picado; Luis Menéndez-Arias; José Antonio Iribarren; Estrella Caballero; Esteban Ribera; Josep Maria Llibre; Bonaventura Clotet; Angels Jaén; David Dalmau; José María Gatel; Joaquín Peraire; Francesc Vidal; Carmen Vidal; Melchor Riera; Juan Córdoba; José López Aldeguer; María José Galindo; Félix Gutiérrez; Marta Álvarez; Federico García; Pilar Pérez-Romero; Pompeyo Viciana; Manuel Leal; José Carlos Palomares; Juan Antonio Pineda; Isabel Viciana; Jesús Santos; Patricia Rodríguez; Juan Luis Gómez Sirvent; Carolina Gutiérrez; Santiago Moreno; Mayte Pérez-Olmeda; José Alcamí; Carmen Rodríguez; Jorge del Romero; Angelina Cañizares; José Pedreira; Celia Miralles; Antonio Ocampo; Luis Morano; Antonio Aguilera; Carolina Garrido; Gustavo Manuzza; Eva Poveda; Vicente Soriano Journal: Retrovirology Date: 2012-08-13 Impact factor: 4.602