Yisong Yue

This paper introduces ALDE, a machine learning-assisted directed evolution method that improves protein engineering outcomes by efficiently exploring epistatic mutation spaces.

This paper introduces DeepCell Types, a language-informed vision model for spatial proteomics cell phenotyping that generalizes across diverse datasets and marker panels.

This study evaluates machine learning-assisted directed evolution strategies across diverse protein fitness landscapes, finding that focused training with zero-shot predictors outperforms random sampling in challenging landscapes.

Polaris provides a deep learning-based pipeline for analyzing spatial transcriptomics data, improving spot detection and gene expression quantification.

This study analyzes transfer learning in protein language models, finding that downstream task performance often relies on early pretraining features rather than scaling with pretraining duration.

Bounding boxes from a transformer detector let the Segment Anything Model segment cells automatically; one generalist model beats generalist Cellpose and matches inter-annotator agreement across mammalian, yeast and bacterial images.

This paper introduces MLDE, a machine learning-assisted directed evolution method that outperforms traditional greedy optimization in navigating combinatorial fitness landscapes with reduced screening burden.