Jun Xia

π-SPECFormer: A Mass Spectrometry-based Foundation Model for Decoding the Dark Proteome

This paper introduces π-SPECFormer, a foundation model for mass spectrometry-based proteomics that decodes the dark proteome by pre-training on unannotated spectra and outperforms baselines in peptide sequencing and modification detection.

Bridging the Gap between Database Search and De Novo Peptide Sequencing with SearchNovo

SearchNovo improves peptide sequencing by combining database search and de novo methods, outperforming existing models on multiple datasets.

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.