Micaela Elisa Consens

This paper introduces EnhancAR, a deep learning model that generates functionally diverse enhancers using evolutionary homologs instead of cell type labels.

This paper introduces a pretraining task for genome language models that predicts evolutionary rate, showing improved performance over sequence-only pretraining.

This paper introduces a framework to interpret attention mechanisms in genomic transformer models by correlating attention scores with biological annotations, showing how biological features are learned through pre-training and fine-tuning.

The paper identifies somatostatin and intra-telencephalic neurons as significantly associated with Alzheimer's disease using bulk and single-nucleus transcriptomics across multiple datasets.