Cong Qi

The paper shows that naive fusion of sequence and graph modalities in TCR-peptide binding prediction can degrade performance and introduces TRACE, a contrastive alignment method that stabilizes multimodal learning.

CellPilot improves single-cell annotation accuracy by integrating small language models into a structured workflow, achieving performance close to larger models while maintaining transparency.

CoLa-VAE improves denoising and representation learning in single-cell data by integrating cell-cell communication information, enhancing downstream analyses like differential communication detection and spatial deconvolution.

This study uses single-cell lineage tracing to map clonal and transcriptional dynamics in melanoma metastasis, identifying distinct transcriptional programs and spatial organization patterns.

This study identifies drug-tolerant persister states in melanoma using MeRLin, showing their role in resistance and tumor recurrence.

This paper introduces TIDE, a dual-encoder framework using cross-attention fusion to predict TCR-pMHC binding, achieving better accuracy than existing methods on benchmark datasets.