Kathleen Chen

This paper introduces cGen, an unsupervised contrastive pre-training method for sequence-based genomic models, showing it improves performance in tasks like chromatin profiling and gene expression prediction.

The paper introduces Sei, a deep learning framework that maps sequence-based regulatory activities to predict variant effects on traits and diseases using human genetics data.

The paper identifies RBP target site dysregulation as a major contributor to psychiatric disorder risk, revealing new insights into disease heritability and providing a resource for further research.

Selene is a PyTorch-based deep learning library for biological sequences, enabling model development, training, and application for various biological data.

PathCORE-T identifies and visualizes globally co-occurring pathways across transcriptomic datasets by analyzing features from machine learning models.

The paper introduces eADAGE, an ensemble method to extract stable expression signatures from public data, revealing a phosphate starvation response in Pseudomonas aeruginosa controlled by PhoB and requiring KinB.