Yutian Liu

STAMP: Single-Cell Transcriptomics Analysis and Multimodal Profiling through Imaging

STAMP enables low-cost, high-throughput single-cell analysis by integrating transcriptomics and proteomics imaging, supporting both single and multimodal profiling of diverse cell types.

The Spatial Atlas of Human Anatomy (SAHA): A Multimodal Subcellular-Resolution Reference Across Human Organs

SAHA creates a multimodal, subcellular-resolution reference atlas of healthy human tissues, revealing spatial organization and disease-associated disruptions across multiple organ systems.

Generative prediction of real-world prevalent SARS-CoV-2 mutation with in silico virus evolution

This paper develops a deep-learning framework to predict real-world prevalent SARS-CoV-2 mutations using in silico virus evolution and protein language models, achieving accurate predictions of future dominant mutations.

Peptide generative design with weakly order-dependent autoregressive language model and lifelong learning

This paper introduces PepGenWOA, a generative model for bioactive peptide design that outperforms existing methods and enables efficient virtual screening.