Madeline Hughes

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.

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

This paper introduces an adaptive resampling method to improve machine learning performance on imbalanced single-cell datasets, showing better results in gene expression reconstruction, cell type classification, and perturbation response prediction.

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.

This paper evaluates deep-learning methods for improving Hi-C resolution using experimentally generated datasets and finds they perform poorly on sparse data, with retraining offering limited improvement.