Chenfeng He

Single-nucleus transcriptome-wide association study of human brain disorders

This study performs a single-nucleus transcriptome-wide association study to identify gene-trait associations in brain disorders, revealing cell-type-specific genetic regulation and ancestry-specific patterns.

Single-Nucleus Atlas of Cell-Type Specific Genetic Regulation in the Human Brain

The paper constructs a single-nucleus atlas of genetic regulation in the human brain, identifying cell-type-specific regulatory effects and disease-associated genes.

Inferring virtual cell environments using multi-agent reinforcement learning

CellTRIP uses multi-agent reinforcement learning to infer dynamic virtual cell environments from single-cell gene expression data, enabling simulation and prediction of cell interactions and changes over time and space.

Pre-training with a rational approach for antibody sequence representation

This study introduces PARA, a pre-trained model for antibody sequences that outperforms existing models on various tasks by incorporating antibody-specific features.

SC-MAMBA2: Leveraging State-Space Models for Efficient Single-Cell Ultra-Long Transcriptome Modeling

SC-MAMBA2 is a single-cell foundation model using state-space models to efficiently process ultra-long transcriptome sequences, outperforming existing models in accuracy and efficiency.

DeepGAMI: Deep biologically guided auxiliary learning for multimodal integration and imputation to improve phenotype prediction

DeepGAMI improves phenotype prediction from multimodal data by integrating biological knowledge and imputing missing modalities, outperforming existing methods and providing interpretable insights.

Brain and Organoid Manifold Alignment (BOMA), a machine learning framework for comparative gene expression analysis across brains and organoids

BOMA identifies conserved and specific developmental gene expression programs between human brains and organoids at cellular resolution.