AIMC Topic: Metagenomics

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Exploring the roles of ribosomal peptides in prokaryote-phage interactions through deep learning-enabled metagenome mining.

Microbiome
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
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...

Genomic language model predicts protein co-regulation and function.

Nature communications
Deciphering the relationship between a gene and its genomic context is fundamental to understanding and engineering biological systems. Machine learning has shown promise in learning latent relationships underlying the sequence-structure-function par...

Waste to resource: Mining antimicrobial peptides in sludge from metagenomes using machine learning.

Environment international
The emergence of antibiotic-resistant bacteria poses a huge threat to the treatment of infections. Antimicrobial peptides are a class of short peptides that widely exist in organisms and are considered as potential substitutes for traditional antibio...

An unsupervised deep learning framework for predicting human essential genes from population and functional genomic data.

BMC bioinformatics
BACKGROUND: The ability to accurately predict essential genes intolerant to loss-of-function (LOF) mutations can dramatically improve the identification of disease-associated genes. Recently, there have been numerous computational methods developed t...

ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning.

PLoS computational biology
The number of published metagenome assemblies is rapidly growing due to advances in sequencing technologies. However, sequencing errors, variable coverage, repetitive genomic regions, and other factors can produce misassemblies, which are challenging...

EnsDeepDP: An Ensemble Deep Learning Approach for Disease Prediction Through Metagenomics.

IEEE/ACM transactions on computational biology and bioinformatics
A growing number of studies show that the human microbiome plays a vital role in human health and can be a crucial factor in predicting certain human diseases. However, microbiome data are often characterized by the limited samples and high-dimension...

Unlocking the microbial studies through computational approaches: how far have we reached?

Environmental science and pollution research international
The metagenomics approach accelerated the study of genetic information from uncultured microbes and complex microbial communities. In silico research also facilitated an understanding of protein-DNA interactions, protein-protein interactions, docking...

The need for an integrated multi-OMICs approach in microbiome science in the food system.

Comprehensive reviews in food science and food safety
Microbiome science as an interdisciplinary research field has evolved rapidly over the past two decades, becoming a popular topic not only in the scientific community and among the general public, but also in the food industry due to the growing dema...

RNN-VirSeeker: A Deep Learning Method for Identification of Short Viral Sequences From Metagenomes.

IEEE/ACM transactions on computational biology and bioinformatics
Viruses are the most abundant biological entities on earth, and play vital roles in many aspects of microbial communities. As major human pathogens, viruses have caused huge mortality and morbidity to human society in history. Metagenomic sequencing ...