Xinzhu Ma

ProDMM is a multimodal sequence modeling framework that captures interdependencies between protein motifs, coding DNA, and regulatory elements to predict phenotypes and generate optimized regulatory sequences.

scDNAm-GPT is a foundation model for single-cell DNA methylation analysis that improves clustering, trajectory inference, and deconvolution using context-aware tokenization and attention mechanisms.

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.

Genomics-FM is a foundation model that reduces reliance on labeled data and outperforms existing models in genome annotation, epigenomic prediction, and variant effect assessment.

BEACON introduces a comprehensive RNA benchmark to evaluate deep learning models, finding that single nucleotide tokenization and ALiBi improve performance.

ProSST integrates protein sequences and structures into a Transformer-based model, achieving state-of-the-art performance in mutation effect prediction and downstream tasks.