Cheng Tan

Base-and-Sugar Dual-Frame Flow Matching for RNA Co-Design

This paper introduces DuetRNA, a dual-frame flow matching model for RNA co-design that improves sequence-structure generation by jointly modeling base-and-sugar frames.

FoldToken4: Consistent & Hierarchical Fold Language

This paper introduces FoldToken4, a model that generates consistent and hierarchical multiscale fold languages for protein structures, reducing redundant training across scales.

FoldToken2: Learning compact, invariant and generative protein structure language

FoldToken2 improves protein structure representation by creating a compact, invariant language that outperforms previous methods in reconstruction accuracy.

Learning Complete Protein Representation by Deep Coupling of Sequence and Structure

This paper introduces CoupleNet, a model that integrates protein sequence and structure to improve representation learning, achieving better performance in fold classification and function prediction.

Structure-Preserving Visualization for Single-cell RNA-Seq Profiles Using Deep Manifold Transformation with Batch-Correction

This paper introduces a visualization method for scRNA-seq data that preserves data structure and corrects batch effects, showing its effectiveness across multiple datasets.

Towards Effective and Generalizable Fine-tuning for Pre-trained Molecular Graph Models

The paper proposes two strategies to improve generalization in fine-tuning pre-trained molecular graph models by augmenting data and regularizing model complexity.