AIMC Topic: Microbiota

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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 ...
Sequence Analysis, DNA Microbiota Autopsy High-Throughput Nucleotide Sequencing Time Factors DNA, Bacterial RNA, Ribosomal, 16S Postmortem Changes Datasets as Topic DNA, Archaeal Aged, 80 and over Young Adult Adult Middle Aged Adolescent Cross-Sectional Studies Male Machine Learning Female Aged Humans
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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...
Female Male Deep Learning Sequence Analysis, RNA Humans Microbiota High-Throughput Nucleotide Sequencing Vagina Forensic Genetics RNA, Ribosomal, 16S Skin Saliva
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MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction.

Methods (San Diego, Calif.) Mar 16, 2019
The human microbiome plays a number of critical roles, impacting almost every aspect of human health and well-being. Conditions in the microbiome have been linked to a number of significant diseases. Additionally, revolutions in sequencing technology...
Humans Metagenomics Machine Learning Metagenome Obesity Algorithms Deep Learning Microbiota Diabetes Mellitus, Type 2
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Incorporating microbial community data with machine learning techniques to predict feed substrates in microbial fuel cells.

Biosensors & bioelectronics Mar 13, 2019
The complicated interactions that occur in mixed-species biotechnologies, including biosensors, hinder chemical detection specificity. This lack of specificity limits applications in which biosensors may be deployed, such as those where an unknown fe...
Machine Learning Wastewater Algorithms Biosensing Techniques Microbiota Carbohydrates Genome, Bacterial Genomics Bacteria Acetates
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Aeromonas hydrophila biofilm, exoprotease, and quorum sensing responses to co-cultivation with diverse foodborne pathogens and food spoilage bacteria on crab surfaces.

Biofouling Jan 30, 2019
The effects of dual species interactions on biofilm formation by Aeromonas hydrophila in the presence of Pseudomonas aeruginosa, Pseudomonas fluorescens, Pectobacterium carotovorum, Salmonella Typhimurium, and Listeria monocytogenes were examined. Hi...
Bacterial Adhesion Quorum Sensing Acyl-Butyrolactones Microbiota Seafood Biofilms Coculture Techniques Animals Brachyura Aeromonas hydrophila Exopeptidases
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Gradients of three coastal environments off the South China Sea and their impacts on the dynamics of heterotrophic microbial communities.

The Science of the total environment Dec 27, 2018
Heterotrophic fungus-like marine protists are recognized to contribute significantly to the coastal carbon cycling largely due to their high biomass and ability to decompose recalcitrant organic matter. Yet, little is known about their dynamics at po...
Bacterial Physiological Phenomena Biomass Heterotrophic Processes Environmental Monitoring Seawater Microscopy, Fluorescence Hong Kong Flow Cytometry Microbiota Fungi
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Embracing Environmental Genomics and Machine Learning for Routine Biomonitoring.

Trends in microbiology Dec 13, 2018
Genomics is fast becoming a routine tool in medical diagnostics and cutting-edge biotechnologies. Yet, its use for environmental biomonitoring is still considered a futuristic ideal. Until now, environmental genomics was mainly used as a replacement ...
DNA Barcoding, Taxonomic Environmental Microbiology Machine Learning Ecosystem Bacteria Genetic Variation Metagenomics Environmental Monitoring Microbiota
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Predicting oral malodour based on the microbiota in saliva samples using a deep learning approach.

BMC oral health Jul 31, 2018
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...
Female Male Humans Saliva High-Throughput Nucleotide Sequencing Microbiota Middle Aged Halitosis Deep Learning RNA, Ribosomal, 16S
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Next-Generation Machine Learning for Biological Networks.

Cell Jun 7, 2018
Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets an...
Drug Discovery Computational Biology Microbiota Drug-Related Side Effects and Adverse Reactions Machine Learning Algorithms Humans Databases, Factual Neural Networks, Computer
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Changes in bacterial and archaeal communities during the concentration of brine at the graduation towers in Ciechocinek spa (Poland).

Extremophiles : life under extreme conditions Dec 19, 2017
This study evaluates the changes in bacterial and archaeal community structure during the gradual evaporation of water from the brine (extracted from subsurface Jurassic deposits) in the system of graduation towers located in Ciechocinek spa, Poland....
Salinity Bacteria Salts Microbiota Archaea Water Microbiology
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