AIMC Topic: RNA, Ribosomal, 16S

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Using gut microbiome metagenomic hypervariable features for diabetes screening and typing through supervised machine learning.

Microbial genomics
Diabetes mellitus is a complex metabolic disorder and one of the fastest-growing global public health concerns. The gut microbiota is implicated in the pathophysiology of various diseases, including diabetes. This study utilized 16S rRNA metagenomic ...

Identification of putative coral pathogens in endangered Caribbean staghorn coral using machine learning.

Environmental microbiology
Coral diseases contribute to the rapid decline in coral reefs worldwide, and yet coral bacterial pathogens have proved difficult to identify because 16S rRNA gene surveys typically identify tens to hundreds of disease-associate bacteria as putative p...

Biomarker Identification for Preterm Birth Susceptibility: Vaginal Microbiome Meta-Analysis Using Systems Biology and Machine Learning Approaches.

American journal of reproductive immunology (New York, N.Y. : 1989)
PROBLEM: The vaginal microbiome has a substantial role in the occurrence of preterm birth (PTB), which contributes substantially to neonatal mortality worldwide. However, current bioinformatics approaches mostly concentrate on the taxonomic classific...

DSNetax: a deep learning species annotation method based on a deep-shallow parallel framework.

Briefings in bioinformatics
Microbial community analysis is an important field to study the composition and function of microbial communities. Microbial species annotation is crucial to revealing microorganisms' complex ecological functions in environmental, ecological and host...

Coracle-a machine learning framework to identify bacteria associated with continuous variables.

Bioinformatics (Oxford, England)
SUMMARY: We present Coracle, an artificial intelligence (AI) framework that can identify associations between bacterial communities and continuous variables. Coracle uses an ensemble approach of prominent feature selection methods and machine learnin...

A comparative study of supervised and unsupervised machine learning algorithms applied to human microbiome.

La Clinica terapeutica
BACKGROUND: The human microbiome, consisting of diverse bacte-rial, fungal, protozoan and viral species, exerts a profound influence on various physiological processes and disease susceptibility. However, the complexity of microbiome data has present...

Achieving pan-microbiome biological insights via the dbBact knowledge base.

Nucleic acids research
16S rRNA amplicon sequencing provides a relatively inexpensive culture-independent method for studying microbial communities. Although thousands of such studies have examined diverse habitats, it is difficult for researchers to use this vast trove of...

DEPP: Deep Learning Enables Extending Species Trees using Single Genes.

Systematic biology
Placing new sequences onto reference phylogenies is increasingly used for analyzing environmental samples, especially microbiomes. Existing placement methods assume that query sequences have evolved under specific models directly on the reference phy...

Human host status inference from temporal microbiome changes via recurrent neural networks.

Briefings in bioinformatics
With the rapid increase in sequencing data, human host status inference (e.g. healthy or sick) from microbiome data has become an important issue. Existing studies are mostly based on single-point microbiome composition, while it is rare that the hos...

Artificial intelligence and metagenomics in intestinal diseases.

Journal of gastroenterology and hepatology
Gut microbiota has been shown to associate with the development of gastrointestinal diseases. In the last decade, development in whole metagenome sequencing and 16S rRNA sequencing technology has dramatically accelerated the gut microbiome's research...