Fusong Ju

GEMGen generates small molecules from transcriptomic data to phenocopy gene knockdown effects and identify novel inhibitors.

FlexRibbon is a pretrained protein model that jointly learns from sequences and structures, achieving state-of-the-art performance in mutation-rich settings.

CellNavi predicts driver genes for cell state transitions using a deep learning framework that integrates gene graphs with transcriptomics data across diverse cell types and perturbations.

The paper introduces {micro}Protein, a framework combining deep learning and reinforcement learning to predict and discover high-gain protein mutants using single mutation data.

CREaTor models cis-regulatory patterns using attention mechanisms and RNA-seq/ChIP-seq data, outperforming existing methods in predicting cCRE-gene interactions across cell types.

KnotFold predicts RNA secondary structures including pseudoknots with higher accuracy than existing methods by combining learned potentials with a minimum-cost flow algorithm.

ProALIGN improves protein structure prediction by learning optimal sequence-template alignments using deep learning, outperforming existing methods on multiple datasets.

CopulaNet directly learns residue co-evolution from multiple sequence alignments to improve protein structure prediction accuracy and efficiency.