Nicolò Fusi

Evaluating the role of pre-training dataset size and diversity on single-cell foundation model performance

This study evaluates how pre-training dataset size and diversity affect single-cell foundation model performance on zero-shot and fine-tuned tasks using 22.2 million cells.

Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data

This paper introduces NCLUSION, a nonparametric clustering method for scRNA-seq that identifies marker genes and clusters simultaneously, showing improved performance and reduced runtime.

The Dayhoff Atlas: scaling sequence diversity for improved protein generation

The paper introduces the Dayhoff Atlas, a resource with large protein sequence datasets and generative models to improve protein design and prediction.

Consequences of training data composition for deep learning models in single-cell biology

This study examines how training data composition affects deep learning models in single-cell transcriptomics, finding poor generalization to new cell types and benefits from including embryonic stem cell differentiation data.

Predicting off-target effects for end-to-end CRISPR guide design

This paper introduces a machine learning model, Elevation, for predicting CRISPR-Cas9 off-target effects, which outperforms existing methods and provides a new evaluation metric.