ArticleFrontiers in genetics2020
Hierarchical Microbial Functions Prediction by Graph Aggregated Embedding.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
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Who cites it
4 citing papers in PubMed.
- A benchmark for environmental microorganism object detection on EMDS-7.Frontiers in microbiology · 2026Article
- Functional redundancy and niche specialization in honeybee and Varroa microbiomes.International microbiology : the official journal of the Spanish Society for Microbiology · 2025Article
- Reorganizing heterogeneous information from host-microbe interaction reveals innate associations among samples.Quantitative biology (Beijing, China) · 2023Article
- Rapid evolution of a novel protective symbiont into keystone taxon in Caenorhabditis elegans microbiota.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Matching 16S rRNA gene sequencing data to a metabolic reference database is a meaningful way to predict the metabolic function of bacteria and archaea, bringing greater insight to the working of the microbial community. However, some operational taxonomy units (OTUs) cannot be functionally profiled, especially for microbial communities from non-human samples cultured in defective media. Therefore, we herein report the development of Hierarchical micrObial functions Prediction by graph aggregated Embedding (HOPE), which utilizes co-occurring patterns and nucleotide sequences to predict microbial functions. HOPE integrates topological structures of microbial co-occurrence networks with
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Registered trials
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