Antimicrobial resistance in rivers: a review of the genes detected and new challenges

P Grenni - Environmental Toxicology and Chemistry, 2022 - Wiley Online Library
River ecosystems are very important parts of the water cycle and an excellent habitat, food,
and drinking water source for many organisms, including humans. Antibiotics are emerging …

Predicting drug-microbiome interactions with machine learning

LE McCoubrey, S Gaisford, M Orlu, AW Basit - Biotechnology advances, 2022 - Elsevier
Pivotal work in recent years has cast light on the importance of the human microbiome in
maintenance of health and physiological response to drugs. It is now clear that …

Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance

H Zhu, H Hao, L Yu - BMC biology, 2023 - Springer
Background Enormous clinical and biomedical researches have demonstrated that
microbes are crucial to human health. Identifying associations between microbes and …

Predicting human microbe–drug associations via graph convolutional network with conditional random field

Y Long, M Wu, CK Kwoh, J Luo, X Li - Bioinformatics, 2020 - academic.oup.com
Motivation Human microbes play critical roles in drug development and precision medicine.
How to systematically understand the complex interaction mechanism between human …

Joint deep autoencoder and subgraph augmentation for inferring microbial responses to drugs

Z Zhou, L Zhuo, X Fu, Q Zou - Briefings in Bioinformatics, 2024 - academic.oup.com
Exploring microbial stress responses to drugs is crucial for the advancement of new
therapeutic methods. While current artificial intelligence methodologies have expedited our …

Ensembling graph attention networks for human microbe–drug association prediction

Y Long, M Wu, Y Liu, CK Kwoh, J Luo, X Li - Bioinformatics, 2020 - academic.oup.com
Motivation Human microbes get closely involved in an extensive variety of complex human
diseases and become new drug targets. In silico methods for identifying potential microbe …

Graph2MDA: a multi-modal variational graph embedding model for predicting microbe–drug associations

L Deng, Y Huang, X Liu, H Liu - Bioinformatics, 2022 - academic.oup.com
Motivation Accumulated clinical studies show that microbes living in humans interact closely
with human hosts, and get involved in modulating drug efficacy and drug toxicity. Microbes …

Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models

L Wang, Y Tan, X Yang, L Kuang… - Briefings in …, 2022 - academic.oup.com
In recent years, with the rapid development of techniques in bioinformatics and life science,
a considerable quantity of biomedical data has been accumulated, based on which …

Inferring human microbe–drug associations via multiple kernel fusion on graph neural network

H Yang, Y Ding, J Tang, F Guo - Knowledge-Based Systems, 2022 - Elsevier
Complex and diverse microbial communities have certain impacts on human health, and
specific drugs are needed to treat diseases caused by microbes. However, most of the …

Predicting microbe–drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy

Z Tian, Y Yu, H Fang, W **e, M Guo - Briefings in Bioinformatics, 2023 - academic.oup.com
Motivation Predicting the associations between human microbes and drugs (MDAs) is one
critical step in drug development and precision medicine areas. Since discovering these …