AIMC Topic: Microbiota

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Artificial intelligence and microbiome research: Evolution of hotspots, research trends, and thematic-based narrative review.

Cellular and molecular biology (Noisy-le-Grand, France)
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
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
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
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
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
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
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
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...

Transition from sulfur autotrophic to mixotrophic denitrification: Performance with different carbon sources, microbial community and artificial neural network modeling.

Chemosphere
To address the limitations inherent in both sulfur autotrophic denitrification (SAD) and heterotrophic denitrification (HD) processes, this study introduces a novel approach. Three carbon sources (glucose, methanol, and sodium acetate) were fed into ...

Microbe-drug association prediction model based on graph convolution and attention networks.

Scientific reports
The human microbiome plays a key role in drug development and precision medicine, but understanding its complex interactions with drugs remains a challenge. Identifying microbe-drug associations not only enhances our understanding of their mechanisms...