ArticleProceedings of the National Academy of Sciences of the United States of America2024
Machine learning enables identification of an alternative yeast galactose utilization pathway.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 24 citations in OpenAlex.
- Machine learning identifies novel signatures of antifungal drug resistance in Saccharomycotina yeasts.PLoS genetics · 2026Article
- Uncorking wine yeast genomics from grape to glass.FEMS yeast research · 2026Review
- The macroevolution of filamentation morphology across the Saccharomycotina yeast subphylum.FEMS yeast research · 2026Article
- The Macroevolution of Filamentation Morphology Across thebioRxiv : the preprint server for biology · 2025Article
- Predicting natural variation in the yeast phenotypic landscape with machine learning.Molecular systems biology · 2025Article
- Celebrating the fifth edition of the International Symposium on Fungal Stress - ISFUS, a decade after its 2014 debut.Fungal biology · 2025Article
- Integrating Intermediate Traits in Phylogenetic Genotype-to-Phenotype Studies.Integrative and comparative biology · 2025Review
- Translating microbial kinetics into quantitative responses and testable hypotheses using Kinbiont.Nature communications · 2025Article
- Machine learning reveals genes impacting oxidative stress resistance across yeasts.Nature communications · 2025Article
- Convergent expansions of keystone gene families drive metabolic innovation in Saccharomycotina yeasts.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Integrating yeast biodiversity and machine learning for predictive metabolic engineering.FEMS yeast research · 2025Review
- Advances and challenges in understanding evolution through genome comparison: meeting report of the European Molecular Biology Organization (EMBO) lecture course "Evolutionary and Comparative Genomics".Bioinformatics advances · 2025Article
- Convergent reductive evolution in bee-associated lactic acid bacteria.Applied and environmental microbiology · 2024Article
- Genomic factors shaping codon usage across the Saccharomycotina subphylum.G3 (Bethesda, Md.) · 2024Article
- Exploring Saccharomycotina Yeast Ecology Through an Ecological Ontology Framework.Yeast (Chichester, England) · 2024Review
- Diverse signatures of convergent evolution in cactus-associated yeasts.PLoS biology · 2024Article
- Convergent reductive evolution in bee-associated lactic acid bacteria.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
10 authors at 6 institutions in 3 countries.
Funding
Abstract
How genomic differences contribute to phenotypic differences is a major question in biology. The recently characterized genomes, isolation environments, and qualitative patterns of growth on 122 sources and conditions of 1,154 strains from 1,049 fungal species (nearly all known) in the yeast subphylum Saccharomycotina provide a powerful, yet complex, dataset for addressing this question. We used a random forest algorithm trained on these genomic, metabolic, and environmental data to predict growth on several carbon sources with high accuracy. Known structural genes involved in assimilation of these sources and presence/absence patterns of growth in other sources were important features contributing to prediction accuracy. By further examining growth on galactose, we found that it can be predicted with high accuracy from either genomic (92.2%) or growth data (82.6%) but not from isolation environment data (65.6%). Prediction accuracy was even higher (93.3%) when we combined genomic and growth data. After the
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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.