Fabiana Rodrigues de Góes

PanTEon provides a cross-kingdom framework for reproducible TE classification, benchmarking machine learning methods and highlighting performance variations across TE superfamilies and lineages.

The paper evaluates feature representations for predicting cancer-associated sORFs in non-coding RNA using traditional machine learning models and finds that k-mer features outperform genomic language model embeddings.

geneRFinder is a machine learning-based gene predictor that outperforms existing tools on metagenomic data, with significant improvements in prediction rates and specificity.