ReviewFrontiers in microbiology2022
Machine Learning and Deep Learning Applications in Metagenomic Taxonomy and Functional Annotation.
Review in Frontiers in microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed.
- Decoding the microbiome: artificial intelligence-targeted gut microenvironment breakthroughs in personalized cancer therapy.Gut microbes · 2026Review
- A new SNP comparison (SnpC) method for detecting unique/enriched gene-SNP variants and comparing population gene mutation diversity.BMC bioinformatics · 2026Article
- Predicting clinical outcome ofMicrobial genomics · 2025Article
- FGeneBERT: function-driven pre-trained gene language model for metagenomics.Briefings in bioinformatics · 2025Article
- Artificial intelligence in biology and medicine.Die Naturwissenschaften · 2025Review
- Biodiscovery ofMicrobial genomics · 2025Article
- Article
- Genome-resolved metagenomics from short-read sequencing data in the era of artificial intelligence.Functional & integrative genomics · 2025Review
- Cutting-edge deep-learning based tools for metagenomic research.National science review · 2025Review
- Reveal your microbes, and i'll reveal your origins: geographical traceability via Scomber colias intestinal tract metagenomics.Animal microbiome · 2025Article
- FGeneBERT: function-driven pre-trained gene language model for metagenomics.Briefings in bioinformatics · 2025Article
- A review of neural networks for metagenomic binning.Briefings in bioinformatics · 2025Review
- Impact of database choice and confidence score on the performance of taxonomic classification using Kraken2.aBIOTECH · 2024Article
- Human Gut Microbiota for Diagnosis and Treatment of Depression.International journal of molecular sciences · 2024Review
- Deep learning methods in metagenomics: a review.Microbial genomics · 2024Review
- metaProbiotics: a tool for mining probiotic from metagenomic binning data based on a language model.Briefings in bioinformatics · 2024Article
- Deep learning in microbiome analysis: a comprehensive review of neural network models.Frontiers in microbiology · 2024Review
- Artificial intelligence and bioinformatics: a journey from traditional techniques to smart approaches.Gastroenterology and hepatology from bed to bench · 2024Review
- A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions.Frontiers in microbiology · 2024Review
- Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Shotgun sequencing of environmental DNA (i.e., metagenomics) has revolutionized the field of environmental microbiology, allowing the characterization of all microorganisms in a sequencing experiment. To identify the microbes in terms of taxonomy and biological activity, the sequenced reads must necessarily be aligned on known microbial genomes/genes. However, current alignment methods are limited in terms of speed and can produce a significant number of false positives when detecting bacterial species or false negatives in specific cases (virus, plasmids, and gene detection). Moreover, recent advances in metagenomics have enabled the reconstruction of new genomes using
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.