AIMC Topic: Microbiota

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MetaPheno: A critical evaluation of deep learning and machine learning in metagenome-based disease prediction.

Methods (San Diego, Calif.)
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...

Incorporating microbial community data with machine learning techniques to predict feed substrates in microbial fuel cells.

Biosensors & bioelectronics
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...

Aeromonas hydrophila biofilm, exoprotease, and quorum sensing responses to co-cultivation with diverse foodborne pathogens and food spoilage bacteria on crab surfaces.

Biofouling
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...

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
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...

Embracing Environmental Genomics and Machine Learning for Routine Biomonitoring.

Trends in microbiology
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 ...

Predicting oral malodour based on the microbiota in saliva samples using a deep learning approach.

BMC oral health
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...

Next-Generation Machine Learning for Biological Networks.

Cell
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...

Changes in bacterial and archaeal communities during the concentration of brine at the graduation towers in Ciechocinek spa (Poland).

Extremophiles : life under extreme conditions
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....

Exploring the Impact of Food on the Gut Ecosystem Based on the Combination of Machine Learning and Network Visualization.

Nutrients
Prebiotics and probiotics strongly impact the gut ecosystem by changing the composition and/or metabolism of the microbiota to improve the health of the host. However, the composition of the microbiota constantly changes due to the intake of daily di...

Microbiome Tools for Forensic Science.

Trends in biotechnology
Microbes are present at every crime scene and have been used as physical evidence for over a century. Advances in DNA sequencing and computational approaches have led to recent breakthroughs in the use of microbiome approaches for forensic science, p...