Self-Attention based model for de-novo antibiotic resistant gene classification with enhanced reliability for out of distribution data detection

Antibiotic resistance monitoring is of paramount importance in the face of this ongoing global epidemic. Using traditional alignment based methods to detect antibiotic resistant genes results in huge number of false negatives. In this paper, we introduce a deep learning model based on a self-attenti...

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Bibliographic Details
Published inbioRxiv
Main Authors Md Nafiz Hamid, Friedberg, Iddo
Format Paper
LanguageEnglish
Published Cold Spring Harbor Cold Spring Harbor Laboratory Press 08.02.2019
Subjects
Antibiotic resistance
Antibiotics
Stochasticity
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Summary:Antibiotic resistance monitoring is of paramount importance in the face of this ongoing global epidemic. Using traditional alignment based methods to detect antibiotic resistant genes results in huge number of false negatives. In this paper, we introduce a deep learning model based on a self-attention architecture that can classify antibiotic resistant genes into correct classes with high precision and recall by just using protein sequences as input. Additionally, deep learning models trained with traditional optimization algorithms (e.g. Adam, SGD) provide poor posterior estimates when tested against Out-of-Distribution (OoD) antibiotic resistant/non-resistant genes. We train our model with an optimization method called Preconditioned Stochastic Gradient Langevin Dynamics (pSGLD) which provides reliable uncertainty estimates when tested against OoD data.
DOI:10.1101/543272