AIMC Topic: Microbiota

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The use of a UV-C disinfection robot in the routine cleaning process: a field study in an Academic hospital.

Antimicrobial resistance and infection control
BACKGROUND: Environmental surface decontamination is a crucial tool to prevent the spread of infections in hospitals. However, manual cleaning and disinfection may be insufficient to eliminate pathogens from contaminated surfaces. Ultraviolet-C (UV-C...

Artificial neural network analysis of microbial diversity in the central and southern Adriatic Sea.

Scientific reports
Bacteria are an active and diverse component of pelagic communities. The identification of main factors governing microbial diversity and spatial distribution requires advanced mathematical analyses. Here, the bacterial community composition was anal...

MiMeNet: Exploring microbiome-metabolome relationships using neural networks.

PLoS computational biology
The advance in microbiome and metabolome studies has generated rich omics data revealing the involvement of the microbial community in host disease pathogenesis through interactions with their host at a metabolic level. However, the computational too...

Prediction of preterm birth based on machine learning using bacterial risk score in cervicovaginal fluid.

American journal of reproductive immunology (New York, N.Y. : 1989)
PROBLEM: Preterm birth (PTB) is a major cause of increased morbidity and mortality in newborns. The main cause of spontaneous PTB (sPTB) is the activation of an inflammatory response as a result of ascending genital tract infection. Despite various s...

Machine learning-assisted immune profiling stratifies peri-implantitis patients with unique microbial colonization and clinical outcomes.

Theranostics
The endemic of peri-implantitis affects over 25% of dental implants. Current treatment depends on empirical patient and site-based stratifications and lacks a consistent risk grading system. We investigated a unique cohort of peri-implantitis patie...

Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox.

Genome biology
The human microbiome is increasingly mined for diagnostic and therapeutic biomarkers using machine learning (ML). However, metagenomics-specific software is scarce, and overoptimistic evaluation and limited cross-study generalization are prevailing i...

Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome.

Nature communications
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...

Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences.

Scientific reports
Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial inte...

Unraveling city-specific signature and identifying sample origin locations for the data from CAMDA MetaSUB challenge.

Biology direct
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...

Systematic evaluation of supervised machine learning for sample origin prediction using metagenomic sequencing data.

Biology direct
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...