AIMC Topic: Microbiota
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DeepMicro: deep representation learning for disease prediction based on microbiome data.
Scientific reports
Apr 7, 2020
Human microbiota plays a key role in human health and growing evidence supports the potential use of microbiome as a predictor of various diseases. However, the high-dimensionality of microbiome data, often in the order of hundreds of thousands, yet ...
Predicting postmortem interval based on microbial community sequences and machine learning algorithms.
Environmental microbiology
Apr 5, 2020
Microbes play an essential role in the decomposition process but were poorly understood in their succession and behaviour. Previous researches have shown that microbes show predictable behaviour that starts at death and changes during the decompositi...
Machine learning methods for microbiome studies.
Journal of microbiology (Seoul, Korea)
Feb 27, 2020
Researches on the microbiome have been actively conducted worldwide and the results have shown human gut bacterial environment significantly impacts on immune system, psychological conditions, cancers, obesity, and metabolic diseases. Thanks to the d...
An unsupervised learning approach to identify novel signatures of health and disease from multimodal data.
Genome medicine
Jan 10, 2020
BACKGROUND: Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. Integrated analysis of data from different modalities has the potential of uncovering novel biomarkers and disease sign...
The damage response framework and infection prevention: From concept to bedside.
Infection control and hospital epidemiology
Jan 9, 2020
Hospital-acquired infections remain a common cause of morbidity and mortality despite advances in infection prevention through use of bundles, environmental cleaning, antimicrobial stewardship, and other best practices. Current prevention strategies ...
OHMI: the ontology of host-microbiome interactions.
Journal of biomedical semantics
Dec 30, 2019
BACKGROUND: Host-microbiome interactions (HMIs) are critical for the modulation of biological processes and are associated with several diseases. Extensive HMI studies have generated large amounts of data. We propose that the logical representation o...
Prediction of microbial communities for urban metagenomics using neural network approach.
Human genomics
Oct 22, 2019
BACKGROUND: Microbes are greatly associated with human health and disease, especially in densely populated cities. It is essential to understand the microbial ecosystem in an urban environment for cities to monitor the transmission of infectious dise...
Challenges in the construction of knowledge bases for human microbiome-disease associations.
Microbiome
Sep 5, 2019
The last few years have seen tremendous growth in human microbiome research, with a particular focus on the links to both mental and physical health and disease. Medical and experimental settings provide initial sources of information about these lin...
An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts.
Marine pollution bulletin
Aug 24, 2019
Machine learning algorithms can be trained on complex data sets to detect, predict, or model specific aspects. Aim of this study was to train an artificial neural network in comparison to a Random Forest model to detect induced changes in microbial c...
Antibiotic resistance and metabolic profiles as functional biomarkers that accurately predict the geographic origin of city metagenomics samples.
Biology direct
Aug 20, 2019
BACKGROUND: The availability of hundreds of city microbiome profiles allows the development of increasingly accurate predictors of the origin of a sample based on its microbiota composition. Typical microbiome studies involve the analysis of bacteria...
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