AIMC Topic: RNA, Ribosomal, 16S

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

mBio Jun 9, 2020
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...
Humans Machine Learning Feces Colonic Neoplasms Microbiota Linear Models Gastrointestinal Diseases Logistic Models RNA, Ribosomal, 16S Predictive Value of Tests
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Machine learning-aided analyses of thousands of draft genomes reveal specific features of activated sludge processes.

Microbiome Feb 11, 2020
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...
Metagenome Sewage Metagenomics Archaea RNA, Ribosomal, 16S Bioreactors Machine Learning Bacteria
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Depression phenotype identified by using single nucleotide exact amplicon sequence variants of the human gut microbiome.

Molecular psychiatry Jan 27, 2020
Single nucleotide exact amplicon sequence variants (ASV) of the human gut microbiome were used to evaluate if individuals with a depression phenotype (DEPR) could be identified from healthy reference subjects (NODEP). Microbial DNA in stool samples o...
RNA, Ribosomal, 16S Nucleotides High-Throughput Nucleotide Sequencing Depression Humans Machine Learning Gastrointestinal Microbiome Phenotype
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Fate of pirlimycin and antibiotic resistance genes in dairy manure slurries in response to temperature and pH adjustment.

The Science of the total environment Dec 26, 2019
Quantifying the fate of antibiotics and antibiotic resistance genes (ARGs) in response to physicochemical factors during storage of manure slurries will aid in efforts to reduce the spread of resistance when manure is land-applied. The objectives of ...
Animals Anti-Bacterial Agents Manure Genes, Bacterial Cattle Drug Resistance, Microbial Hydrogen-Ion Concentration Lactation Female Temperature RNA, Ribosomal, 16S Clindamycin
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An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts.

Marine pollution bulletin Aug 24, 2019
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...
Neural Networks, Computer Machine Learning Algorithms Microbiota Glycine High-Throughput Nucleotide Sequencing Random Allocation Environmental Monitoring RNA, Ribosomal, 16S Water Pollutants, Chemical Glyphosate Water Microbiology
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Machine learning to predict microbial community functions: An analysis of dissolved organic carbon from litter decomposition.

PloS one Jul 1, 2019
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...
Carbon Microbiota Machine Learning Bacteria RNA, Ribosomal, 16S Sequence Analysis, DNA
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Coupling growth kinetics modeling with machine learning reveals microbial immigration impacts and identifies key environmental parameters in a biological wastewater treatment process.

Microbiome Apr 17, 2019
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...
Microbial Consortia Bioreactors Kinetics Sewage Bacteria RNA, Ribosomal, 16S Machine Learning Wastewater
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Machine learning performance in a microbial molecular autopsy context: A cross-sectional postmortem human population study.

PloS one Apr 15, 2019
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 ...
Microbiota Autopsy High-Throughput Nucleotide Sequencing DNA, Archaeal Postmortem Changes Datasets as Topic DNA, Bacterial RNA, Ribosomal, 16S Sequence Analysis, DNA Humans Middle Aged Machine Learning Young Adult Adolescent Female Adult Cross-Sectional Studies Time Factors Aged Aged, 80 and over Male
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Novel taxonomy-independent deep learning microbiome approach allows for accurate classification of different forensically relevant human epithelial materials.

Forensic science international. Genetics Apr 4, 2019
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...
High-Throughput Nucleotide Sequencing Vagina Forensic Genetics Skin Saliva Microbiota Deep Learning Sequence Analysis, RNA RNA, Ribosomal, 16S Female Male Humans
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Metabarcoding and machine learning analysis of environmental DNA in ballast water arriving to hub ports.

Environment international Jan 17, 2019
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 Los Angeles DNA Barcoding, Taxonomic Water Microbiology RNA, Ribosomal, 16S South Africa Introduced Species China Singapore Ships Bacteria Escherichia coli
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