AIMC Topic: Metagenome
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Machine Learning Meta-analysis of Large Metagenomic Datasets: Tools and Biological Insights.
PLoS computational biology
Jul 11, 2016
Shotgun metagenomic analysis of the human associated microbiome provides a rich set of microbial features for prediction and biomarker discovery in the context of human diseases and health conditions. However, the use of such high-resolution microbia...
Large-scale machine learning for metagenomics sequence classification.
Bioinformatics (Oxford, England)
Nov 20, 2015
MOTIVATION: Metagenomics characterizes the taxonomic diversity of microbial communities by sequencing DNA directly from an environmental sample. One of the main challenges in metagenomics data analysis is the binning step, where each sequenced read i...
DectICO: an alignment-free supervised metagenomic classification method based on feature extraction and dynamic selection.
BMC bioinformatics
Oct 7, 2015
BACKGROUND: Continual progress in next-generation sequencing allows for generating increasingly large metagenomes which are over time or space. Comparing and classifying the metagenomes with different microbial communities is critical. Alignment-free...
MetaVelvet-SL: an extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning.
DNA research : an international journal for rapid publication of reports on genes and genomes
Nov 27, 2014
The assembly of multiple genomes from mixed sequence reads is a bottleneck in metagenomic analysis. A single-genome assembly program (assembler) is not capable of resolving metagenome sequences, so assemblers designed specifically for metagenomics ha...
Genome-resolved metagenomics from short-read sequencing data in the era of artificial intelligence.
Functional & integrative genomics
Jun 10, 2025
Genome-resolved metagenomics is a computational method that enables researchers to reconstruct microbial genomes from a given sample directly. This process involves three major steps, i.e. (i) preprocessing of the reads (ii) metagenome assembly, and ...
XenoBug: machine learning-based tool to predict pollutant-degrading enzymes from environmental metagenomes.
NAR genomics and bioinformatics
Jun 1, 2025
Application of machine learning-based methods to identify novel bacterial enzymes capable of degrading a wide range of xenobiotics offers enormous potential for bioremediation of toxic and carcinogenic recalcitrant xenobiotics such as pesticides, pla...
Deciphering the biosynthetic potential of microbial genomes using a BGC language processing neural network model.
Nucleic acids research
Apr 10, 2025
Biosynthetic gene clusters (BGCs), key in synthesizing microbial secondary metabolites, are mostly hidden in microbial genomes and metagenomes. To unearth this vast potential, we present BGC-Prophet, a transformer-based language model for BGC predict...
A review of neural networks for metagenomic binning.
Briefings in bioinformatics
Mar 4, 2025
One of the main goals of metagenomic studies is to describe the taxonomic diversity of microbial communities. A crucial step in metagenomic analysis is metagenomic binning, which involves the (supervised) classification or (unsupervised) clustering o...
DeepES: deep learning-based enzyme screening to identify orphan enzyme genes.
Bioinformatics (Oxford, England)
Mar 4, 2025
MOTIVATION: Progress in sequencing technology has led to determination of large numbers of protein sequences, and large enzyme databases are now available. Although many computational tools for enzyme annotation were developed, sequence information i...
Using gut microbiome metagenomic hypervariable features for diabetes screening and typing through supervised machine learning.
Microbial genomics
Mar 1, 2025
Diabetes mellitus is a complex metabolic disorder and one of the fastest-growing global public health concerns. The gut microbiota is implicated in the pathophysiology of various diseases, including diabetes. This study utilized 16S rRNA metagenomic ...
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