Georgina S. Stegmayer

This study evaluates scRNA-seq foundation models for plant perturbation response prediction, finding that performance varies significantly with evaluation protocol and that preserving gene identity improves generalization.

The paper evaluates cross-validation strategies for RNA secondary structure prediction, showing that current methods bias results and proposing homology-aware strategies for fair comparison.

emb2dis predicts protein disorder using a deep learning model combining ResNets and dilated convolutions, achieving top performance on benchmark datasets.

LLPSight predicts LLPS-driving proteins using machine learning and protein language models, achieving an F1 score of 0.885 and identifying promising targets in the human proteome.

GNN2Pfam uses graph neural networks to improve Pfam domain annotation by integrating protein sequence and structure data, outperforming HMM-based methods.

RNAcomp2D provides a web tool to run multiple RNA secondary structure prediction methods and compare their results visually.

ET-Pfam improves protein family prediction by combining transfer learning and ensemble strategies, achieving lower error rates than individual models and competitors.

sincFold is a deep learning model that predicts RNA secondary structure contact matrices from sequences, outperforming existing methods on benchmark datasets.

This paper introduces gGN, a method that represents graph nodes as low-rank Gaussian distributions, showing improved performance in preserving graph structures and measuring semantic similarity.

This paper introduces a convolutional deep residual neural network for predicting pre-miRNAs with high precision across multiple genomes.