AI Medical Compendium Topic

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RNA, Ribosomal, 16S

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A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems.

mBio
Machine learning (ML) modeling of the human microbiome has the potential to identify microbial biomarkers and aid in the diagnosis of many diseases such as inflammatory bowel disease, diabetes, and colorectal cancer. Progress has been made toward dev...

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...

An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts.

Marine pollution bulletin
Machine learning algorithms can be trained on complex data sets to detect, predict, or model specific aspects. Aim of this study was to train an artificial neural network in comparison to a Random Forest model to detect induced changes in microbial c...

Compositional data network analysis via lasso penalized D-trace loss.

Bioinformatics (Oxford, England)
MOTIVATION: With the development of high-throughput sequencing techniques for 16S-rRNA gene profiling, the analysis of microbial communities is becoming more and more attractive and reliable. Inferring the direct interaction network among microbial c...

Novel taxonomy-independent deep learning microbiome approach allows for accurate classification of different forensically relevant human epithelial materials.

Forensic science international. Genetics
Correct identification of different human epithelial materials such as from skin, saliva and vaginal origin is relevant in forensic casework as it provides crucial information for crime reconstruction. However, the overlap in human cell type composit...

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...

Metabarcoding and machine learning analysis of environmental DNA in ballast water arriving to hub ports.

Environment international
While ballast water has long been linked to the global transport of invasive species, little is known about its microbiome. Herein, we used 16S rRNA gene sequencing and metabarcoding to perform the most comprehensive microbiological survey of ballast...

Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition.

PloS one
Microbial communities are ubiquitous and often influence macroscopic properties of the ecosystems they inhabit. However, deciphering the functional relationship between specific microbes and ecosystem properties is an ongoing challenge owing to the c...

Machine learning performance in a microbial molecular autopsy context: A cross-sectional postmortem human population study.

PloS one
BACKGROUND: The postmortem microbiome can provide valuable information to a death investigation and to the human health of the once living. Microbiome sequencing produces, in general, large multi-dimensional datasets that can be difficult to analyze ...

Predicting oral malodour based on the microbiota in saliva samples using a deep learning approach.

BMC oral health
BACKGROUND: Oral malodour is mainly caused by volatile sulphur compounds produced by bacteria and bacterial interactions. It is difficult to predict the presence or absence of oral malodour based on the abundances of specific species and their combin...