Asa Ben‐Hur

This paper reports on the fourth CAFA challenge, showing improvements in Gene Ontology term prediction but highlighting challenges in new evaluation settings.

This paper introduces a supervised foundation model for plant regulatory genomics that predicts chromatin state from genomic sequence, showing improved speed, accuracy, and interpretability over DNA language models.

This study evaluates attention-based methods for detecting transcription factor cooperativity, showing that entropy-enhanced models improve interpretability and precision.

The paper uses large-scale deep learning models to study intron retention, showing that chromatin state plays a key role in its regulation.

The paper introduces a graph neural network to predict small-molecule inhibition of protein complexes using compound and protein structure inputs, achieving high accuracy on benchmark datasets.

The paper investigates how transcription factors regulate intron retention through open chromatin analysis and deep learning models, finding zinc finger factors are prevalent in intron retention events.

SATORI is a self-attention model that detects regulatory element interactions, identifying more experimentally verified TF-TF interactions than existing methods without requiring post-processing.