ArticleScientific reports2024
DeepGOMeta for functional insights into microbial communities using deep learning-based protein function prediction.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Harnessing synthetic microbial communities for desert vegetation restoration.Synthetic and systems biotechnology · 2027Review
- Integrating multi-omics technologies to decipher microbiome functions.Nature communications · 2026Review
- Evaluating completeness, coherence, and consistency of genome-scale function annotations.Briefings in bioinformatics · 2026Review
- Decoding extremophiles: insights from bioinformatics, machine learning, and data-driven approaches.Briefings in bioinformatics · 2026Review
- 16S rRNA sequence captures microbial functional potential.bioRxiv : the preprint server for biology · 2026Article
- DA-HGL: a domain-augmented heterogeneous graph learning framework for protein function prediction.Briefings in bioinformatics · 2025Article
- Protein function prediction using GO similarity-based heterogeneous network propagation.Scientific reports · 2025Article
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Authors and funding
4 authors.
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Abstract
Analyzing microbial samples remains computationally challenging due to their diversity and complexity. The lack of robust de novo protein function prediction methods exacerbates the difficulty in deriving functional insights from these samples. Traditional prediction methods, dependent on homology and sequence similarity, often fail to predict functions for novel proteins and proteins without known homologs. Moreover, most of these methods have been trained on largely eukaryotic data, and have not been evaluated on or applied to microbial datasets. This research introduces DeepGOMeta, a deep learning model designed for protein function prediction as Gene Ontology (GO) terms, trained on a dataset relevant to microbes. The model is applied to diverse microbial datasets to demonstrate its use for gaining biological insights. Data and code are available at https://github.com/bio-ontology-research-group/deepgometa.
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