AI Medical Compendium Journal:
Microbiome

Showing 1 to 10 of 10 articles

Modeling microbiome-trait associations with taxonomy-adaptive neural networks.

Microbiome
The human microbiome, a complex ecosystem of microorganisms inhabiting the body, plays a critical role in human health. Investigating its association with host traits is essential for understanding its impact on various diseases. Although shotgun met...

DRAMMA: a multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data.

Microbiome
BACKGROUND: Antibiotics are essential for medical procedures, food security, and public health. However, ill-advised usage leads to increased pathogen resistance to antimicrobial substances, posing a threat of fatal infections and limiting the benefi...

Effects of data transformation and model selection on feature importance in microbiome classification data.

Microbiome
BACKGROUND: Accurate classification of host phenotypes from microbiome data is crucial for advancing microbiome-based therapies, with machine learning offering effective solutions. However, the complexity of the gut microbiome, data sparsity, composi...

Exploring the roles of ribosomal peptides in prokaryote-phage interactions through deep learning-enabled metagenome mining.

Microbiome
BACKGROUND: Microbial secondary metabolites play a crucial role in the intricate interactions within the natural environment. Among these metabolites, ribosomally synthesized and post-translationally modified peptides (RiPPs) are becoming a promising...

ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences.

Microbiome
BACKGROUND: Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identific...

HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genes.

Microbiome
BACKGROUND: The spread of antibiotic resistance has become one of the most urgent threats to global health, which is estimated to cause 700,000 deaths each year globally. Its surrogates, antibiotic resistance genes (ARGs), are highly transmittable be...

Machine learning-aided analyses of thousands of draft genomes reveal specific features of activated sludge processes.

Microbiome
BACKGROUND: Microorganisms in activated sludge (AS) play key roles in the wastewater treatment processes. However, their ecological behaviors and differences from microorganisms in other environments have mainly been studied using the 16S rRNA gene t...

Challenges in the construction of knowledge bases for human microbiome-disease associations.

Microbiome
The last few years have seen tremendous growth in human microbiome research, with a particular focus on the links to both mental and physical health and disease. Medical and experimental settings provide initial sources of information about these lin...

Coupling growth kinetics modeling with machine learning reveals microbial immigration impacts and identifies key environmental parameters in a biological wastewater treatment process.

Microbiome
BACKGROUND: Ubiquitous in natural and engineered ecosystems, microbial immigration is one of the mechanisms shaping community assemblage. However, quantifying immigration impact remains challenging especially at individual population level. The activ...

Tracking antibiotic resistance gene pollution from different sources using machine-learning classification.

Microbiome
BACKGROUND: Antimicrobial resistance (AMR) has been a worldwide public health concern. Current widespread AMR pollution has posed a big challenge in accurately disentangling source-sink relationship, which has been further confounded by point and non...