Application of machine learning techniques to tuberculosis drug resistance analysis by Kouchaki, S, Yang, Y, Walker, T, Walker, A, Wilson, D, Peto, T, Crook, D, Clifton, D, Cryptic Consortium

An end-to-end heterogeneous graph attention network for Mycobacterium tuberculosis drug-resistance prediction by Yang, Y, Walker, TM, Kouchaki, S, Wang, C, Peto, TEA, Crook, D, CRYPTIC Consortium, Clifton, D

DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis by Yang, Y, Walker, T, Walker, A, Wilson, D, Peto, T, Crook, D, Shamout, F, Cryptic Consortium, Zhu, T, Clifton, D

Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach by Barilar, I, Battaglia, S, Borroni, E, Zankin, A, Clifton, D, Crook, D, Earle, S, Fowler, PW, Giberto Cruz, A, Hoosdally, S, Knaggs, J, Hunt, M, Kouchaki, S, Lachapelle, A, Peto, T, Rodger, G, Roohi, A, Thwaites, G, Walker, AS, Wilson, D, Yang, Y

Other Authors: “…Cryptic Consortium…”