AIMC Topic: Microbiota
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A graph neural network approach for predicting drug susceptibility in the human microbiome.
Computers in biology and medicine
Jul 1, 2024
Recent studies have illuminated the critical role of the human microbiome in maintaining health and influencing the pharmacological responses of drugs. Clinical trials, encompassing approximately 150 drugs, have unveiled interactions with the gastroi...
High-performing cross-dataset machine learning reveals robust microbiota alteration in secondary apical periodontitis.
Frontiers in cellular and infection microbiology
Jun 21, 2024
Multiple research groups have consistently underscored the intricate interplay between the microbiome and apical periodontitis. However, the presence of variability in experimental design and quantitative assessment have added a layer of complexity, ...
Deep learning for predicting 16S rRNA gene copy number.
Scientific reports
Jun 20, 2024
Culture-independent 16S rRNA gene metabarcoding is a commonly used method for microbiome profiling. To achieve more quantitative cell fraction estimates, it is important to account for the 16S rRNA gene copy number (hereafter 16S GCN) of different co...
Editorial: Machine learning and deep learning applications in pathogenic microbiome research.
Frontiers in cellular and infection microbiology
Jun 20, 2024
Discovery of antimicrobial peptides in the global microbiome with machine learning.
Cell
Jun 5, 2024
Novel antibiotics are urgently needed to combat the antibiotic-resistance crisis. We present a machine-learning-based approach to predict antimicrobial peptides (AMPs) within the global microbiome and leverage a vast dataset of 63,410 metagenomes and...
Exploring the roles of ribosomal peptides in prokaryote-phage interactions through deep learning-enabled metagenome mining.
Microbiome
May 24, 2024
BACKGROUND: Microbial secondary metabolites play a crucial role in the intricate interactions within the natural environment. Among these metabolites, ribosomally synthesized and post-translationally modified peptides (RiPPs) are becoming a promising...
Deciphering the microbial landscape of lower respiratory tract infections: insights from metagenomics and machine learning.
Frontiers in cellular and infection microbiology
May 23, 2024
BACKGROUND: Lower respiratory tract infections represent prevalent ailments. Nonetheless, current comprehension of the microbial ecosystems within the lower respiratory tract remains incomplete and necessitates further comprehensive assessment. Lever...
Multi-class boosting for the analysis of multiple incomplete views on microbiome data.
BMC bioinformatics
May 14, 2024
BACKGROUND: Microbiome dysbiosis has recently been associated with different diseases and disorders. In this context, machine learning (ML) approaches can be useful either to identify new patterns or learn predictive models. However, data to be fed t...
A culture-independent approach, supervised machine learning, and the characterization of the microbial community composition of coastal areas across the Bay of Bengal and the Arabian Sea.
BMC microbiology
May 10, 2024
BACKGROUND: Coastal areas are subject to various anthropogenic and natural influences. In this study, we investigated and compared the characteristics of two coastal regions, Andhra Pradesh (AP) and Goa (GA), focusing on pollution, anthropogenic acti...
Identifying and characterization of novel broad-spectrum bacteriocins from the Shanxi aged vinegar microbiome: Machine learning, molecular simulation, and activity validation.
International journal of biological macromolecules
May 9, 2024
Shanxi aged vinegar microbiome encodes a wide variety of bacteriocins. The aim of this study was to mine, screen and characterize novel broad-spectrum bacteriocins from the large-scale microbiome data of Shanxi aged vinegar through machine learning, ...
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