ArticleBioMed research international2017
Gene Prediction in Metagenomic Fragments with Deep Learning.
Article in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Multi-feature fusion for gene prediction and functional peptide identification.Frontiers in microbiology · 2026Article
- Review
- Analysis of metagenomic data.Nature reviews. Methods primers · 2025Article
- Deep learning methods in metagenomics: a review.Microbial genomics · 2024Review
- Deep learning in microbiome analysis: a comprehensive review of neural network models.Frontiers in microbiology · 2024Review
- A toolbox of machine learning software to support microbiome analysis.Frontiers in microbiology · 2023Review
- Current challenges and best-practice protocols for microbiome analysis.Briefings in bioinformatics · 2021Review
- Linking Gut Microbiome and Lipid Metabolism: Moving beyond Associations.Metabolites · 2021Review
- A review of computational tools for generating metagenome-assembled genomes from metagenomic sequencing data.Computational and structural biotechnology journal · 2021Review
- Microbial Community Changes in the Rhizosphere Soil of Healthy and RustyBioMed research international · 2020Article
- Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks.Frontiers in microbiology · 2019Review
- Identification of Antioxidant Proteins With Deep Learning From Sequence Information.Frontiers in pharmacology · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features) and using deep stacking networks learning model, we present a novel method (called Meta-MFDL) to predict the metagenomic genes. The results with 10 CV and independent tests show that Meta-MFDL is a powerful tool for identifying genes from metagenomic fragments.
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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.