AIMC Topic: RNA, Ribosomal, 16S

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Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network.

PLoS computational biology Sep 22, 2021
Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for micr...
Phenotype Inflammatory Bowel Diseases Deep Learning Computational Biology Humans Neural Networks, Computer Algorithms Microbiota Proof of Concept Study Host Microbial Interactions Prevotella Gastrointestinal Microbiome RNA, Ribosomal, 16S Databases, Genetic Natural Language Processing
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Microbiome Preprocessing Machine Learning Pipeline.

Frontiers in immunology Jun 18, 2021
BACKGROUND: 16S sequencing results are often used for Machine Learning (ML) tasks. 16S gene sequences are represented as feature counts, which are associated with taxonomic representation. Raw feature counts may not be the optimal representation for ...
Bacteria Progesterone Gastrointestinal Microbiome RNA, Ribosomal, 16S Animals Phylogeny Mice Mucositis Hematopoietic Stem Cell Transplantation Interleukin-1alpha Colitis Humans Neural Networks, Computer Machine Learning Pregnancy Female Adult
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Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome.

Nature communications Mar 26, 2021
The stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of drugs such as proton pump inhibitors (PPIs), or bacterial infection such as Helicobacter pylori, cause significant microbial alterations. Ye...
RNA, Ribosomal, 16S Population Dynamics Humans Helicobacter pylori Helicobacter Infections Machine Learning Microbiota Bacteria Proton Pump Inhibitors Gastrointestinal Microbiome Stomach
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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...
Humans Machine Learning Phenotype RNA, Ribosomal, 16S Metagenomics Metagenome
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Bacterial endosymbiont inhabiting leaves and their antioxidant and antidiabetic potential.

Journal of complementary & integrative medicine Dec 22, 2020
OBJECTIVES: Research on endosymbionts is emerging globally and is considered as a potential source of bioactive phytochemicals. The present study examines the antioxidant and antidiabetic of the endophytic crude extract isolated from leaves.
Plant Extracts Animals Plant Leaves Rats Bacteria Diabetes Mellitus, Experimental Hypoglycemic Agents Antioxidants RNA, Ribosomal, 16S
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Identification the source of fecal contamination for geographically unassociated samples with a statistical classification model based on support vector machine.

Journal of hazardous materials Dec 11, 2020
The bacterial diversity and corresponding biological significance revealed by high-throughput sequencing contribute massive information to source tracking of fecal contamination. The performances of classification models on predicting the fecal sourc...
Water Pollution Water Microbiology Bacteroidetes Humans Feces Support Vector Machine RNA, Ribosomal, 16S
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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...
Metagenome RNA, Ribosomal, 16S Supervised Machine Learning Microbiota Metagenomics
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Host variables confound gut microbiota studies of human disease.

Nature Nov 4, 2020
Low concordance between studies that examine the role of microbiota in human diseases is a pervasive challenge that limits the capacity to identify causal relationships between host-associated microorganisms and pathology. The risk of obtaining false...
Adult Male Middle Aged Diet Case-Control Studies Female RNA, Ribosomal, 16S Alcohol Drinking Humans Feces Aged Residence Characteristics Disease Machine Learning ROC Curve Aged, 80 and over Area Under Curve Life Style Data Analysis Young Adult Gastrointestinal Motility Confounding Factors, Epidemiologic Diabetes Mellitus, Type 2 Body Mass Index Gastrointestinal Microbiome
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Temporal variation and sharing of antibiotic resistance genes between water and wild fish gut in a peri-urban river.

Journal of environmental sciences (China) Oct 24, 2020
Antibiotic resistance genes (ARGs) as emergence contaminations have spread widely in the water environment. Wild fish may be recipients and communicators of ARGs in the water environment, however, the distribution and transmission of ARGs in the wild...
Anti-Bacterial Agents Drug Resistance, Microbial Animals Genes, Bacterial Water Rivers Gastrointestinal Microbiome RNA, Ribosomal, 16S
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Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data.

Communications biology Jul 15, 2020
The study of complex microbial communities typically entails high-throughput sequencing and downstream bioinformatics analyses. Here we expand and accelerate microbiota analysis by enabling cell type diversity quantification from multidimensional flo...
Humans Neural Networks, Computer Biomass Escherichia coli Microbiota Biodiversity High-Throughput Screening Assays RNA, Ribosomal, 16S Water Microbiology Flow Cytometry Supervised Machine Learning
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