Apr 18, 2020

Transfer learning improves antibiotic resistance class prediction

BioRxiv : the Preprint Server for Biology
M. N. Hamid, Iddo Friedberg

Abstract

Motivation: Antibiotic resistance is a growing public health problem, which affects millions of people worldwide, and if left unchecked is expected to upend many aspects of healthcare as it is practiced today. Identifying the type of antibiotic resistant genes in genome and metagenomic sample is of utmost importance in the prevention, diagnosis, and treatment of infections. Today there are multiple tools available that predict antibiotic resistance class from DNA and protein sequences, yet there is a lack of benchmarks on the performances of these tools. Results: We have developed a dataset that is curated from 15 available databases, and annotated with their antibiotic class labels. We also developed a transfer learning approach with neural networks, TRAC, that outperforms existing antiobiotic resistance prediction tools. While TRAC provides the current state-of-the-art performance, we hope our newly developed dataset will also provide the community with a much needed standardized dataset to develop novel methods that can predict antibiotic resistance class with superior prediction performance. Availability: TRAC is available on github https://github.com/nafizh/TRAC and the datasets are available at figshare https://doi.org/10...Continue Reading

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Mentioned in this Paper

Metabolic Process, Cellular
Study
7-fluorobenzanthracene
Metabolic Networks
Hypertrophic Cardiomyopathy
Nutrients
Metabolic Marker
Metabolic Pathway
Analysis
Bacteria, Anaerobic

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