Exploration of machine learning algorithms for predicting the changes in abundance of antibiotic resistance genes in anaerobic digestion-Reference-Cited by-同舟云学术

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Exploration of machine learning algorithms for predicting the changes in abundance of antibiotic resistance genes in anaerobic digestion

Published:2022-09 Issue: Volume:839 Page:156211
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Haffiez Nervana,Chung Tae Hyun,Zakaria Basem S.,Shahidi Manjila,Mezbahuddin Symon,Maal-Bared Rasha,Dhar Bipro Ranjan

Funder

NSERC

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference80 articles.

1. Evaluation of antibiotic resistance dissemination by wastewater treatment plant effluents with different catchment areas in Germany;Alexander;Sci. Rep.,2020

2. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data;Arango-Argoty;Microbiome,2018

3. Thermal hydrolysis of sludge counteracts polystyrene nanoplastics-induced stress during anaerobic digestion;Azizi;ACS ES&T Eng.,2022

4. Sludge anaerobic digestion with high concentrations of tetracyclines and sulfonamides: dynamics of microbial communities and change of antibiotic resistance genes;Bai;Bioresour. Technol.,2019

5. Advances towards understanding and engineering direct interspecies electron transfer in anaerobic digestion;Barua;Bioresour. Technol.,2017

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