ArticleScientific reports2023
Machine learning classifiers predict key genomic and evolutionary traits across the kingdoms of life.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 17 citations in OpenAlex.
- Deep learning based attention enhanced phylogenetic radial basis function networks (AE-PRBFN) for genomic codon usage classification across species.Scientific reports · 2026Article
- Protein language models are accidental taxonomists.BMC bioinformatics · 2026Article
- GGAR: gradient guided adaptive regularization enhances deep learning classification of brassica species using codon usage bias.BMC bioinformatics · 2026Article
- An Alignment Free Framework for Taxonomic Inference From Codon and Codon-Pair Usage.Ecology and evolution · 2026Article
- Integrative genomic and machine learning approaches reveal evolutionary signatures in the winged bean mitochondrial genome.BMC plant biology · 2025Article
- Protein Language Models are Accidental Taxonomists.bioRxiv : the preprint server for biology · 2025Article
- Gradient responsive regularization: a deep learning framework for codon frequency based classification of evolutionarily conserved genes.BMC genomic data · 2025Article
- Deep learning decodes species-specific codon usage signatures in Brassica from coding sequences.Scientific reports · 2025Article
- Codon usage bias is presumably affected by tRNA selection effects in Actinidia polyploidization events.BMC genomics · 2025Article
- PRCFX-DT: a new graph-based approach for feature selection and classification of genomic sequences.BMC bioinformatics · 2025Article
- Cafe: Improved Federated Data Imputation by Leveraging Missing Data Heterogeneity.IEEE transactions on knowledge and data engineering · 2025Article
- MICROPHERRET: MICRObial PHEnotypic tRait ClassifieR using Machine lEarning Techniques.Environmental microbiome · 2024Article
- Identifying biomarkers related to motor function in chronic stroke: A fNIRS and TMS study.CNS neuroscience & therapeutics · 2024Article
- In Silico Analysis of the Phylogenetic and Physiological Characteristics of Sphingobium indicum B90A: A Hexachlorocyclohexane-Degrading Bacterium.Current microbiology · 2024Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
In this study, we investigate how an organism's codon usage bias can serve as a predictor and classifier of various genomic and evolutionary traits across the domains of life. We perform secondary analysis of existing genetic datasets to build several AI/machine learning models. When trained on codon usage patterns of nearly 13,000 organisms, our models accurately predict the organelle of origin and taxonomic identity of nucleotide samples. We extend our analysis to identify the most influential codons for phylogenetic prediction with a custom feature ranking ensemble. Our results suggest that the genetic code can be utilized to train accurate classifiers of taxonomic and phylogenetic features. We then apply this classification framework to open reading frame (ORF) detection. Our statistical model assesses all possible ORFs in a nucleotide sample and rejects or deems them plausible based on the codon usage distribution. Our dataset and analyses are made publicly available on GitHub and the UCI ML Repository to facilitate open-source reproducibility and community engagement.
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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.