AIMC Topic: Metagenomics

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Machine learning for the analysis of multi-omics data.

Methods (San Diego, Calif.) Feb 9, 2021
Machine Learning Epigenomics Proteomics Metagenomics Gene Expression Profiling Genomics
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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...
RNA, Ribosomal, 16S Metagenomics Metagenome Humans Machine Learning Phenotype
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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...
Metagenomics Metagenome Cities Microbiota Humans Machine Learning
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Deep learning for HGT insertion sites recognition.

BMC genomics Dec 29, 2020
BACKGROUND: Horizontal Gene Transfer (HGT) refers to the sharing of genetic materials between distant species that are not in a parent-offspring relationship. The HGT insertion sites are important to understand the HGT mechanisms. Recent studies in m...
Deep Learning Metagenomics Base Sequence Phylogeny Genome Gene Transfer, Horizontal
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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...
Metagenome Machine Learning Cities Metagenomics Geography Microbiota Humans
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Deep learning in next-generation sequencing.

Drug discovery today Oct 12, 2020
Next-generation sequencing (NGS) methods lie at the heart of large parts of biological and medical research. Their fundamental importance has created a continuously increasing demand for processing and analysis methods of the data sets produced, addr...
Metagenomics Deep Learning High-Throughput Nucleotide Sequencing Neural Networks, Computer Humans Biomedical Research
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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...
Genome, Bacterial Metagenome Sequence Analysis, DNA Bacteria Bayes Theorem Gastrointestinal Microbiome Metagenomics Machine Learning Algorithms Humans
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Metatranscriptomics reveals the gene functions and metabolic properties of the major microbial community during Chinese Sichuan Paocai fermentation.

Food microbiology Jul 3, 2020
Chinese Sichuan Paocai (CSP) is one of the world's best-known fermented vegetables with a large presence in the Chinese market. The dynamic microbial community is the main contributor to Paocai fermentation. However, little is known about the ecologi...
Metagenomics Fermentation Vegetables Food Microbiology Brassica Microbiota Transcriptome Fermented Foods Enterobacteriaceae China Lactobacillaceae
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Predicting host taxonomic information from viral genomes: A comparison of feature representations.

PLoS computational biology May 26, 2020
The rise in metagenomics has led to an exponential growth in virus discovery. However, the majority of these new virus sequences have no assigned host. Current machine learning approaches to predicting virus host interactions have a tendency to focus...
Viruses Algorithms Caudovirales Databases, Factual Linear Models Phylogeny Genome, Viral Nucleotides Metagenomics Support Vector Machine Bacteria Sequence Analysis, DNA Computational Biology Area Under Curve
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