Xuting Zhang

Hi! I am Xuting Zhang, a third-year undergraduate at Westlake University, advised by Prof. Jianyang Zeng. In Fall 2025, I visited Duke University as an exchange student, where I was fortunate to conduct research on virtual drug screening under the supervision of Prof. Rohit Singh. Previously, during my freshman year, I worked with Prof. Fajie Yuan on computational protein modeling, and with Prof. Yanxiao Zhang on aging research through the analysis of single-cell RNA-seq data from mice.

My research interests lie at the intersection of machine learning and life sciences (AI4Science). I primarily focus on developing novel computational methods for biological discovery, including the construction of multi-omics models and computational protein engineering for applications in protein interaction and design. Additionally, I am passionate about developing new algorithms for single-cell and spatial transcriptomics and leveraging them for clinical data analysis.

news

Oct 24, 2025	“Democratizing Protein Language Model Training, Sharing and Collaboration” is published at *Nature Biotechnology*!
Oct 02, 2025	“A tri-modal protein language model enables advanced protein searches” is published at *Nature Biotechnology*!

selected publications

Nat. Biotech.

A trimodal protein language model enables advanced protein searches

Jin Su^†, Yan He^†, Shiyang You^†, Shiyu Jiang, Xibin Zhou, Xuting Zhang, Yuxuan Wang, Xining Su, Igor Tolstoy, Xing Chang, and others

Nature Biotechnology, 2025

Bib HTML Code Website

@article{su2025trimodal,
  title = {A trimodal protein language model enables advanced protein searches},
  author = {Su, Jin and He, Yan and You, Shiyang and Jiang, Shiyu and Zhou, Xibin and Zhang, Xuting and Wang, Yuxuan and Su, Xining and Tolstoy, Igor and Chang, Xing and others},
  journal = {Nature Biotechnology},
  pages = {1--7},
  year = {2025},
  publisher = {Nature Publishing Group US New York},
}

Nat. Biotech.

Democratizing protein language model training, sharing and collaboration

Jin Su, Zhikai Li, Tianli Tao, Chenchen Han, Yan He, Fengyuan Dai, Qingyan Yuan, Yuan Gao, Tong Si, Xuting Zhang, and others

Nature Biotechnology, 2025

Bib HTML Code Website

@article{su2025democratizing,
  title = {Democratizing protein language model training, sharing and collaboration},
  author = {Su, Jin and Li, Zhikai and Tao, Tianli and Han, Chenchen and He, Yan and Dai, Fengyuan and Yuan, Qingyan and Gao, Yuan and Si, Tong and Zhang, Xuting and others},
  journal = {Nature Biotechnology},
  pages = {1--7},
  year = {2025},
  publisher = {Nature Publishing Group US New York},
}

bioRxiv
STARNet enables spatially resolved inference of gene regulatory networks from spatial multi-omics data

Lei Hu^†, Shichen Zhang^†, Xuting Zhang^†, Yihai Luo^†, Haoteng Gu, Peng Liu, Sheng Mao, Li Chen, Yuhao Xia, Minghao Yang, and others

bioRxiv, 2025

Abs Bib PDF Website

Biological tissues are composed of distinct microenvironments that spatially orchestrate gene expression and cell identity. However, the regulatory principles governing domain-specific cellular functions remain poorly understood due to the lack of effective methods for mapping gene regulatory networks (GRNs) in situ. To address this gap, we introduce STARNet, a representation learning approach that leverages heterogeneous hypergraph modeling of spatial transcriptomic and epigenomic data to resolve tissue-domain-specific regulatory interactions. By integrating graph neural networks with contrastive learning in a self-supervised framework, STARNet learns unified embeddings that preserve both multi-modal molecular features and anatomical spatial context, enabling accurate and domain-resolved GRN reconstruction within complex tissues. Benchmarking on both simulated and real datasets demonstrates that STARNet achieves state-of-the-art performance. We further demonstrate its broad applicability across diverse biological contexts, including neural development, genetic disease risk, and drug-induced developmental toxicity. In the mouse brain, it delineates region-specific regulatory networks and reconstructs spatiotemporal programs underlying neural stem cell differentiation. In human genetics, it provides a mechanistic link between genotypes and phenotypes by showing how genome-wide association study (GWAS) variants for complex diseases perturb hippocampus-specific GRNs. In developmental toxicology, STARNet reveals that drug-induced disruptions of GRNs in defined embryonic regions underlie tissue-specific vulnerability. Collectively, STARNet offers a powerful and versatile framework for resolving the spatial regulatory logic of complex tissues, providing multi-angle insights into tissue patterning, development, and disease mechanisms.
@article{hu2025starnet, title = {STARNet enables spatially resolved inference of gene regulatory networks from spatial multi-omics data}, author = {Hu, Lei and Zhang, Shichen and Zhang, Xuting and Luo, Yihai and Gu, Haoteng and Liu, Peng and Mao, Sheng and Chen, Li and Xia, Yuhao and Yang, Minghao and others}, journal = {bioRxiv}, pages = {2025--08}, year = {2025}, publisher = {Cold Spring Harbor Laboratory}, }