AIMC Topic: Metagenome

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Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox.

Genome biology Mar 30, 2021
The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing i...
Databases, Genetic Models, Statistical Meta-Analysis as Topic ROC Curve Computational Biology Gastrointestinal Microbiome Metagenomics Confounding Factors, Epidemiologic Metagenome Workflow Crohn Disease Microbiota Humans Machine Learning Software
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Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences.

Scientific reports Feb 25, 2021
Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial inte...
Data Analysis Phenotype Female Humans Artificial Intelligence Aging Middle Aged Deep Learning Aged Young Adult Adult Smokers Menopause Metagenome Microbiota Biodiversity Computational Biology Metagenomics Skin
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MegaR: an interactive R package for rapid sample classification and phenotype prediction using metagenome profiles and machine learning.

BMC bioinformatics Jan 18, 2021
BACKGROUND: Diverse microbiome communities drive biogeochemical processes and evolution of animals in their ecosystems. Many microbiome projects have demonstrated the power of using metagenomics to understand the structures and factors influencing th...
Metagenome Phenotype RNA, Ribosomal, 16S Metagenomics Humans Machine Learning
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Unraveling city-specific signature and identifying sample origin locations for the data from CAMDA MetaSUB challenge.

Biology direct Jan 4, 2021
BACKGROUND: Composition of microbial communities can be location-specific, and the different abundance of taxon within location could help us to unravel city-specific signature and predict the sample origin locations accurately. In this study, the wh...
Metagenome Humans Machine Learning Cities Microbiota Metagenomics
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Systematic evaluation of supervised machine learning for sample origin prediction using metagenomic sequencing data.

Biology direct Dec 10, 2020
BACKGROUND: The advent of metagenomic sequencing provides microbial abundance patterns that can be leveraged for sample origin prediction. Supervised machine learning classification approaches have been reported to predict sample origin accurately wh...
RNA, Ribosomal, 16S Supervised Machine Learning Microbiota Metagenomics Metagenome
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A machine learning framework to determine geolocations from metagenomic profiling.

Biology direct Nov 23, 2020
BACKGROUND: Studies on metagenomic data of environmental microbial samples found that microbial communities seem to be geolocation-specific, and the microbiome abundance profile can be a differentiating feature to identify samples' geolocations. In t...
Microbiota Metagenomics Geography Metagenome Humans Machine Learning Cities
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A generalized machine-learning aided method for targeted identification of industrial enzymes from metagenome: A xylanase temperature dependence case study.

Biotechnology and bioengineering Nov 14, 2020
Growing industrial utilization of enzymes and the increasing availability of metagenomic data highlight the demand for effective methods of targeted identification and verification of novel enzymes from various environmental microbiota. Xylanases are...
Machine Learning Animals Hot Temperature Rumen Cattle Metagenome Endo-1,4-beta Xylanases Microbiota Sheep
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Keeping up with the genomes: efficient learning of our increasing knowledge of the tree of life.

BMC bioinformatics Sep 21, 2020
BACKGROUND: It is a computational challenge for current metagenomic classifiers to keep up with the pace of training data generated from genome sequencing projects, such as the exponentially-growing NCBI RefSeq bacterial genome database. When new ref...
Bayes Theorem Metagenomics Humans Bacteria Metagenome Genome, Bacterial Gastrointestinal Microbiome Machine Learning Sequence Analysis, DNA Algorithms
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Machine Learning Strategy for Gut Microbiome-Based Diagnostic Screening of Cardiovascular Disease.

Hypertension (Dallas, Tex. : 1979) Sep 10, 2020
Cardiovascular disease (CVD) is the number one leading cause for human mortality. Besides genetics and environmental factors, in recent years, gut microbiota has emerged as a new factor influencing CVD. Although cause-effect relationships are not cle...
Gastrointestinal Microbiome Metagenome Cardiovascular Diseases Feces Humans Mass Screening Machine Learning
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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...
Metagenomics RNA, Ribosomal, 16S Archaea Machine Learning Bioreactors Metagenome Bacteria Sewage
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