Fan Jiang

VenusGenome: Genome-Aware Masked Autoencoding for Microbial Genome Representation Learning

VenusGenome improves microbial genome representation learning by using a genome-aware masked autoencoder, achieving high performance in phenotype prediction and gene-level tasks.

Discovery of Expression-Governing Residues in Proteins

This study introduces Venus-TIGER, a deep learning model that identifies critical amino acids for protein expression, showing improved accuracy and transferability across datasets.

Venus-MAXWELL: Efficient Learning of Protein-Mutation Stability Landscapes using Protein Language Models

The paper introduces Venus-MAXWELL, a framework for predicting protein mutant stability using sequence-to-landscape learning, achieving higher accuracy and faster inference than existing methods.

CACIMAR: Cross-species Analysis of Cell Identities, Markers, Regulations and Interactions Using Single-cell RNA Sequencing Data

CACIMAR identifies conserved cell types, markers, regulations, and interactions across species using scRNA-seq data.