AIMC Topic: Microbiota
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Feasibility study of machine learning to explore relationships between antimicrobial resistance and microbial community structure in global wastewater treatment plant sludges.
Bioresource technology
Nov 26, 2024
Wastewater sludges (WSs) are major reservoirs and emission sources of antibiotic resistance genes (ARGs) in cities. Identifying antimicrobial resistance (AMR) host bacteria in WSs is crucial for understanding AMR formation and mitigating biological a...
Human limits in machine learning: prediction of potato yield and disease using soil microbiome data.
BMC bioinformatics
Nov 26, 2024
BACKGROUND: The preservation of soil health is a critical challenge in the 21st century due to its significant impact on agriculture, human health, and biodiversity. We provide one of the first comprehensive investigations into the predictive potenti...
Artificial intelligence and microbiome research: Evolution of hotspots, research trends, and thematic-based narrative review.
Cellular and molecular biology (Noisy-le-Grand, France)
Nov 24, 2024
Artificial intelligence (AI) and microbiome have emerged in recent years as transformative fields with far-reaching implications for various biomedical domains. This paper presents a comprehensive bibliometric analysis examining the intersection of A...
Deep learning enabled integration of tumor microenvironment microbial profiles and host gene expressions for interpretable survival subtyping in diverse types of cancers.
mSystems
Nov 20, 2024
The tumor microbiome, a complex community of microbes found in tumors, has been found to be linked to cancer development, progression, and treatment outcome. However, it remains a bottleneck in distangling the relationship between the tumor microbiom...
Artificial Intelligence-Assisted Automatic Raman-Activated Cell Sorting (AI-RACS) System for Mining Specific Functional Microorganisms in the Microbiome.
Analytical chemistry
Nov 11, 2024
The microbiome represents the natural presence of microorganisms, and exploring, understanding, and leveraging its functions will bring about significant breakthroughs in life sciences and applications. Raman-activated cell sorting (RACS) enables the...
Application of tongue image characteristics and oral-gut microbiota in predicting pre-diabetes and type 2 diabetes with machine learning.
Frontiers in cellular and infection microbiology
Nov 4, 2024
BACKGROUND: This study aimed to characterize the oral and gut microbiota in prediabetes mellitus (Pre-DM) and type 2 diabetes mellitus (T2DM) patients while exploring the association between tongue manifestations and the oral-gut microbiota axis in d...
GEMimp: An Accurate and Robust Imputation Method for Microbiome Data Using Graph Embedding Neural Network.
Journal of molecular biology
Oct 26, 2024
Microbiome research has increasingly underscored the profound link between microbial compositions and human health, with numerous studies establishing a strong correlation between microbiome characteristics and various diseases. However, the analysis...
Machine learning models reveal how polycyclic aromatic hydrocarbons influence environmental bacterial communities.
The Science of the total environment
Oct 22, 2024
Polycyclic aromatic hydrocarbons (PAHs) are harmful and widespread pollutants in the environment, posing an ecological threat. However, exploring the influence of PAHs on environmental bacterial communities in different habitats (soil, water, and sed...
LineageFilter: Improved Proteotyping of Complex Samples Using Metaproteomics and Machine Learning.
Journal of proteome research
Oct 19, 2024
Metaproteomics is a powerful tool to characterize how microbiota function by analyzing their proteic content by tandem mass spectrometry. Given the complexity of these samples, accurately assessing their taxonomical composition without prior informat...
Development and evaluation of statistical and artificial intelligence approaches with microbial shotgun metagenomics data as an untargeted screening tool for use in food production.
mSystems
Oct 10, 2024
UNLABELLED: The increasing knowledge of microbial ecology in food products relating to quality and safety and the established usefulness of machine learning algorithms for anomaly detection in multiple scenarios suggests that the application of micro...
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