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Cited by 7 publications
(3 citation statements)
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“…A review gives a brief on ten studies which have used ML algorithms to predict AMR. In one study, 23 mycobacterial genes (including eis, gidB, rrs, tlyA, rspL rspA, gyrA, ahpC, fabG1, inhA, katG, rpoB, embB, pnca) and their surrounding base-pairs were used as features to develop machine learning models using different algorithms which were then validated using DA predictions ( Walker et al, 2015 ) ( Sharma et al, 2022 ). Another study retrieved 222 prominent features for resistance prediction using the Multi-task Wide and Deep Neural Network with fastq files obtained from whole genome sequencing which showed high efficacy.…”
Section: Introductionmentioning
confidence: 99%
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“…A review gives a brief on ten studies which have used ML algorithms to predict AMR. In one study, 23 mycobacterial genes (including eis, gidB, rrs, tlyA, rspL rspA, gyrA, ahpC, fabG1, inhA, katG, rpoB, embB, pnca) and their surrounding base-pairs were used as features to develop machine learning models using different algorithms which were then validated using DA predictions ( Walker et al, 2015 ) ( Sharma et al, 2022 ). Another study retrieved 222 prominent features for resistance prediction using the Multi-task Wide and Deep Neural Network with fastq files obtained from whole genome sequencing which showed high efficacy.…”
Section: Introductionmentioning
confidence: 99%
“…Another study retrieved 222 prominent features for resistance prediction using the Multi-task Wide and Deep Neural Network with fastq files obtained from whole genome sequencing which showed high efficacy. This was followed by development of another model—Wide and Deep Neural Network model, with still better accuracy ( Green et al, 2022 ) ( Sharma et al, 2022 ). Other algorithms used for this purpose and which showed high accuracy include Classification Trees and Gradient Boosted Trees, which was used to unravel new mutations which can concur resistance to fourteen drugs.…”
Section: Introductionmentioning
confidence: 99%
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