Jing Gong

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.

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.

AIDO.Tissue: Spatial Cell-Guided Pretraining for Scalable Spatial Transcriptomics Foundation Model

AIDO.Tissue is a spatial transcriptomics foundation model that uses cell-guided pretraining to improve spatial cell type classification, niche prediction, and density estimation.

Large Scale Foundation Model on Single-cell Transcriptomics

This paper introduces scFoundation, a large-scale pretrained model for single-cell transcriptomics, achieving state-of-the-art results in multiple downstream tasks.

Training Compute-Optimal Protein Language Models

This paper investigates optimal training of protein language models by analyzing the relationship between model size, training data, and performance, finding that diverse training data and scaling laws improve results.

xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein

A 100-billion-parameter protein language model trained with masked and autoregressive objectives together, topping prior results on 15 of 18 benchmarks and powering a fast structure predictor.

xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data

xTrimoGene is a scalable transformer model for scRNA-seq data that reduces computational demands while maintaining accuracy, achieving state-of-the-art results in downstream tasks.