Pengtao Xie

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

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

scLong is a billion-parameter foundation model that captures long-range gene context in scRNA-seq by modeling all genes and integrating gene knowledge, outperforming existing models in various tasks.

MetaboliteChat is a multimodal LLM that integrates molecular graphs and images with natural language to provide interactive metabolite analysis and functional insights.

RobPicker improves macromolecule identification in cryo-ET by addressing label noise and class imbalance through a meta-learning framework, outperforming existing methods in detecting small and rare species.

GeneChat predicts gene function from DNA sequences using a multi-modal large language model, outperforming GPT-4o on standard metrics.

ProteinAligner improves protein function and property prediction by integrating sequence, structure, and literature text modalities in a multi-modal pretraining framework.

DrugChat, a multi-modal LLM, predicts drug mechanisms and properties through free-form text, outperforming GPT-4 and specialized models in accuracy and versatility.

ProteinChat is a multi-modal large language model that predicts protein functions from amino acid sequences, outperforming general-purpose LLMs and task-specific models.

This paper introduces MALDmef, a method for inferring multiple-wave admixture by modeling LD decay with multiple exponential functions, showing improved accuracy over existing methods in simulated and real data.