ArticleCommunications biology2025
Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Beyond the Classics: The Synergy of AI and Genomics Reveals an Expanded Repertoire of Pigmentation Genes.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2026Review
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- Functional diversity and ecological consequences of endophytic Bacillus-plant interactions.Folia microbiologica · 2026Review
- Article
- Advancing FAIR data towards comparable, organized, predictive AI-ready data for community validation.Communications biology · 2026Review
- Microbe Decoder uncovers functional traits of microbes in microbiome datasets.Nucleic acids research · 2026Article
- Enhanced identification of key bacterial motility genes via a cross-species genomic hybrid feature machine learning approach.BMC genomics · 2026Article
- Diversity, ecology, cell biology and evolution of the Asgard archaea.Nature reviews. Microbiology · 2026Review
- Bacterial proteome foundation model enhances functional prediction from enzymes to ecological interactions.bioRxiv : the preprint server for biology · 2026Article
- Noncanonical genetic markers resolve the pre-GOE emergence of aerobic bacteria in Earth's history.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- metaTraits: a large-scale integration of microbial phenotypic trait information.Nucleic acids research · 2026Article
- Genome-based reassessment ofCurrent research in microbial sciences · 2026Article
- tsAMP: a strain-level antimicrobial peptide identification framework based on large language models and pathogen genomic variation.Frontiers in microbiology · 2026Article
- Rapid key gene discovery for bacterial shape: a cross-species machine learning approach.BMC microbiology · 2025Article
- Machine learning prediction of bacterial optimal growth temperature from protein domain signatures reveals thermoadaptation mechanisms.BMC genomics · 2025Article
- Bacterial Systematic Genetics and Integrated Multi-Omics: Beyond Static Genomics Toward Predictive Models.International journal of molecular sciences · 2025Review
- BacDive in 2025: the core database for prokaryotic strain data.Nucleic acids research · 2025Article
- Discovery and cultivation of prokaryotic taxa in the age of metagenomics and artificial intelligence.The ISME journal · 2025Article
- Explainable rule-based prediction of cultivation media for microbes.Computational and structural biotechnology journal · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Predicting prokaryotic phenotypes-observable traits that govern functionality, adaptability, and interactions-holds significant potential for fields such as biotechnology, environmental sciences, and evolutionary biology. In this study, we leverage machine learning to explore the relationship between prokaryotic genotypes and phenotypes. Utilizing the highly standardized datasets in the BacDive database, we model eight physiological properties based on protein family inventories, evaluate model performance using multiple metrics, and examine the biological implications of our predictions. The high confidence values achieved underscore the importance of data quality and quantity for reliably inferring bacterial phenotypes. Our approach generates 50,396 completely new datapoints for 15,938 strains, now openly available in the BacDive database, thereby enriching existing phenotypic resources and enabling further research. The open-source software we provide can be readily applied to other datasets, such as those from metagenomic studies, and to various applications, including assessing the potential of soil bacteria for bioremediation.
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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.