Kathleen Chen

Contrastive pre-training for sequence based genomics models

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.

A sequence-based global map of regulatory activity for deciphering human genetics

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.

Genome-wide landscape of RNA-binding protein dysregulation reveals a major impact on psychiatric disorder risk

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: a PyTorch-based deep learning library for biological sequence-level data

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

PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia

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

Unsupervised extraction of stable expression signatures from public compendia with eADAGE

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.