Eric P. Xing

Pretraining Enhances Megabase-Scale Gene Expression Prediction with GeneUnet

The paper introduces GB.GeneUnet, a transformer-based model pretrained on multi-species genomes to predict gene expression at megabase-scale contexts, achieving state-of-the-art performance.

Cell-Level Virtual Screening

This paper introduces CellVS-Net, a cell-level virtual screening model that outperforms existing methods on two benchmarks, demonstrating its viability as an alternative to molecular-level drug screening.

Harnessing AI to Build Virtual Cells

This paper introduces VCHarness, an AI system that automatically builds perturbation-response models for virtual cells, outperforming manual methods and reducing development time.

Using AI to Build AI: AIDO.Builder Enables Autonomous Machine Learning Model Building for Biomedicine

AIDO.Builder automates biomedical model development by generating executable pipelines, selecting strategies, and iteratively refining models without manual intervention.

GenBio-PathFM: A State-of-the-Art Foundation Model for Histopathology

GenBio-PathFM is a foundation model for histopathology that achieves state-of-the-art performance with less training data by using a diverse data curation pipeline and a dual-stage learning strategy.

Foundation Models Improve Perturbation Response Prediction

This study evaluates foundation models for predicting cellular responses to perturbations, finding that some pretrained embeddings improve predictions while others do not.