AIMC Topic: Microbiota
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Biomarker Identification for Preterm Birth Susceptibility: Vaginal Microbiome Meta-Analysis Using Systems Biology and Machine Learning Approaches.
American journal of reproductive immunology (New York, N.Y. : 1989)
Jul 1, 2024
PROBLEM: The vaginal microbiome has a substantial role in the occurrence of preterm birth (PTB), which contributes substantially to neonatal mortality worldwide. However, current bioinformatics approaches mostly concentrate on the taxonomic classific...
Highly accurate classification and discovery of microbial protein-coding gene functions using FunGeneTyper: an extensible deep learning framework.
Briefings in bioinformatics
May 23, 2024
High-throughput DNA sequencing technologies decode tremendous amounts of microbial protein-coding gene sequences. However, accurately assigning protein functions to novel gene sequences remain a challenge. To this end, we developed FunGeneTyper, an e...
Deep learning methods in metagenomics: a review.
Microbial genomics
Apr 1, 2024
The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such a...
DSNetax: a deep learning species annotation method based on a deep-shallow parallel framework.
Briefings in bioinformatics
Mar 27, 2024
Microbial community analysis is an important field to study the composition and function of microbial communities. Microbial species annotation is crucial to revealing microorganisms' complex ecological functions in environmental, ecological and host...
Understanding gut microbiome-based machine learning platforms: A review on therapeutic approaches using deep learning.
Chemical biology & drug design
Mar 1, 2024
Human beings possess trillions of microbial cells in a symbiotic relationship. This relationship benefits both partners for a long time. The gut microbiota helps in many bodily functions from harvesting energy from digested food to strengthening bioc...
A comparative study of supervised and unsupervised machine learning algorithms applied to human microbiome.
La Clinica terapeutica
Jan 1, 2024
BACKGROUND: The human microbiome, consisting of diverse bacte-rial, fungal, protozoan and viral species, exerts a profound influence on various physiological processes and disease susceptibility. However, the complexity of microbiome data has present...
Achieving pan-microbiome biological insights via the dbBact knowledge base.
Nucleic acids research
Jul 21, 2023
16S rRNA amplicon sequencing provides a relatively inexpensive culture-independent method for studying microbial communities. Although thousands of such studies have examined diverse habitats, it is difficult for researchers to use this vast trove of...
Predicting potential microbe-disease associations based on multi-source features and deep learning.
Briefings in bioinformatics
Jul 20, 2023
Studies have confirmed that the occurrence of many complex diseases in the human body is closely related to the microbial community, and microbes can affect tumorigenesis and metastasis by regulating the tumor microenvironment. However, there are sti...
DEPP: Deep Learning Enables Extending Species Trees using Single Genes.
Systematic biology
May 19, 2023
Placing new sequences onto reference phylogenies is increasingly used for analyzing environmental samples, especially microbiomes. Existing placement methods assume that query sequences have evolved under specific models directly on the reference phy...
Tracing human life trajectory using gut microbial communities by context-aware deep learning.
Briefings in bioinformatics
Jan 19, 2023
The gut microbial communities are highly plastic throughout life, and the human gut microbial communities show spatial-temporal dynamic patterns at different life stages. However, the underlying association between gut microbial communities and time-...
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