AIMC Topic: Microbiota

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DEPP: Deep Learning Enables Extending Species Trees using Single Genes.

Systematic biology
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
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-...

DeepToA: an ensemble deep-learning approach to predicting the theater of activity of a microbiome.

Bioinformatics (Oxford, England)
MOTIVATION: Metagenomics is the study of microbiomes using DNA sequencing. A microbiome consists of an assemblage of microbes that is associated with a 'theater of activity' (ToA). An important question is, to what degree does the taxonomic and funct...

Virtifier: a deep learning-based identifier for viral sequences from metagenomes.

Bioinformatics (Oxford, England)
MOTIVATION: Viruses, the most abundant biological entities on earth, are important components of microbial communities, and as major human pathogens, they are responsible for human mortality and morbidity. The identification of viral sequences from m...

phyLoSTM: a novel deep learning model on disease prediction from longitudinal microbiome data.

Bioinformatics (Oxford, England)
MOTIVATION: Research shows that human microbiome is highly dynamic on longitudinal timescales, changing dynamically with diet, or due to medical interventions. In this article, we propose a novel deep learning framework 'phyLoSTM', using a combinatio...

Human host status inference from temporal microbiome changes via recurrent neural networks.

Briefings in bioinformatics
With the rapid increase in sequencing data, human host status inference (e.g. healthy or sick) from microbiome data has become an important issue. Existing studies are mostly based on single-point microbiome composition, while it is rare that the hos...

CoCoNet: an efficient deep learning tool for viral metagenome binning.

Bioinformatics (Oxford, England)
MOTIVATION: Metagenomic approaches hold the potential to characterize microbial communities and unravel the intricate link between the microbiome and biological processes. Assembly is one of the most critical steps in metagenomics experiments. It con...

DeePhage: distinguishing virulent and temperate phage-derived sequences in metavirome data with a deep learning approach.

GigaScience
BACKGROUND: Prokaryotic viruses referred to as phages can be divided into virulent and temperate phages. Distinguishing virulent and temperate phage-derived sequences in metavirome data is important for elucidating their different roles in interactio...