2019
DOI: 10.1093/bioinformatics/btz067
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DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis

Abstract: Motivation Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages. Results We used a large cohort of TB p… Show more

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Cited by 43 publications
(36 citation statements)
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“…This curve can be used to determine the optimal sensitivity for a given specificity, or vice versa. Furthermore, one can use the Area Under this Curve (AUC) to evaluate and compare the performance of different models, as is commonly done in drug resistance studies [10, 20, 21].…”
Section: Resultsmentioning
confidence: 99%
“…This curve can be used to determine the optimal sensitivity for a given specificity, or vice versa. Furthermore, one can use the Area Under this Curve (AUC) to evaluate and compare the performance of different models, as is commonly done in drug resistance studies [10, 20, 21].…”
Section: Resultsmentioning
confidence: 99%
“…They are being adopted rapidly for disease diagnosis and analysis of data from x-ray, CT, and MRI imaging. There are an increasing number of papers now employing deep learning methods for diagnosis of NTDs (e.g., Gao and Qian, 2018 ; Ting et al, 2018 ; Khalighifar et al, 2019 ; Rajaraman et al, 2019 ; Yang et al, 2019 ; Fuhad et al, 2020 ) that are beyond the scope of this review. Although deep learning methods offer state-of-the-art performance in modelling the biological properties of drug-like data for next generation drugs ( Ferreira and Andricopulo, 2019 ; Lavecchia, 2019 ), they have not yet been widely adopted by the NTD research community.…”
Section: Introductionmentioning
confidence: 99%
“…Traditionally, estimating TB drug resistance has been based on known and biologically established mutations [3,10]. However, as resistance phenotype prediction from genomic data is a binary classification problem with high-dimensional input-a standard task in statistical and machine learning (ML)-various such techniques have been applied to antibiotic resistance recently [19][20][21][22][23][24][25][26]. Employing ML in genomic phenotype prediction has two main advantages.…”
Section: Introductionmentioning
confidence: 99%
“…Employing ML in genomic phenotype prediction has two main advantages. First, several recent studies have shown that these techniques can at least compete with existing direct association methods based on mechanistic and evidential knowledge, which has been curated and scrutinised for decades [20][21][22][24][25][26]. Second, examining important feature sets of the trained models might hint at yet unexplored variants leading to novel discoveries or reveal latent multidimensional interactions (e.g.…”
Section: Introductionmentioning
confidence: 99%