Artificial Intelligence Enables Comparative Single-Cell Genomics
Time
10:00 AM, June 2, 2026 ( Beijing )3:00 AM, June 2, 2026 ( Italy )
9:00 PM, June 1, 2026 ( New York )
Contact Us
Email: cbmjournal@sciexplor.comSpeaker
Prof. Shihua Zhang
State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.
Prof. Shihua Zhang is a Professor at the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, and a Professor at the University of Chinese Academy of Sciences. His research focuses on bioinformatics, computational biology, and artificial intelligence, with contributions to single-cell and spatial omics, 3D genomics, and machine learning. His work has been published in leading journals including Cell, Nature Genetics, Nature Computational Science, Nature Communications, Cell Genomics, Cell Systems, IEEE TPAMI, and JMLR. He has received numerous honors, including the China Youth Science and Technology Award (2013), the Lu Jiaxi Young Talent Award of the CAS (2013), Zhongchuang Software Talent Award (2022), the National Science Fund for Excellent Young Scholars (2014), and the National High-level Talent Program (2022, 2018). His achievements have also been recognized among the Top Ten Advances in Bioinformatics in China (2021, 2022). He currently serves as a Section Editor for PLOS Computational Biology and an Editorial Board Member of Genomics, Proteomics & Bioinformatics.
Host
Prof. De-Shuang Huang
Ningbo Key Laboratory of Multi-Omics & Multimodal Biomedical Data Mining and Computing, Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, Zhejiang, China.
Prof. De-Shuang Huang is a PhD Supervisor at Ningbo Oriental Institute of Technology. He is a Foreign Member of the Russian Academy of Engineering and an IEEE Fellow, as well as a Fellow of several international organizations, including the International Association for Pattern Recognition (IAPR), the Asia-Pacific Artificial Intelligence Association (AAIA), and the International Artificial Intelligence Industry Alliance (AIIA). He is the founding Editor-in-Chief of the journal Computational Biomedicine and serves as the Director of the Ningbo Key Laboratory of Multi-Omics and Multimodal Biomedical Data Mining and Computing (Category A). He is also the Chair of the Biomedical Data Mining and Computing Committee of the (proposed) Chinese Society of Bioinformatics. He was selected for the “Hundred Talents Program” of the Chinese Academy of Sciences in 2000 and serves as Chief Scientist (Principal Investigator) of a major project under China’s New Generation Artificial Intelligence Program. His research achievements have been published in a number of internationally authoritative journals such as Nature Communications, Advanced Science, Genome Biology, Bioinformatics, and Briefings in Bioinformatics.
Introduction
Single-cell and spatial transcriptomics data from multiple species present remarkable opportunities to explore cellular origins and evolution. However, integrating and annotating these data across different species remains challenging due to the variations in biological techniques, ambiguity of homologous relationships, and limited biological knowledge. To this end, we first design a heterogeneous graph neural network model, CAME, to learn aligned and interpretable cell and gene embeddings for cross-species cell type assignment and gene module extraction from single-cell transcriptomics data. We further develop CAMEX, which leverages many-to-many homologous relationships for multi-species integration, alignment, and annotation of scRNA-seq data from multiple species. CAMEX outperforms state-of-the-art methods integration on various cross-species benchmarking datasets (ranging from one to eleven species). CAMEX facilitates the alignment of diverse species across different developmental stages, significantly enhancing our understanding of organ and organism origins. CAMEX enables the detection of species-specific cell types and marker genes through cell and gene embedding. We have extended the concept of comparative (single-cell) transcriptomics to the spatial level. We develop BrainAlign for the whole-brain alignment of spatial transcriptomics between humans and mice and STACAME for deciphering shared and divergent tissue architectures from cross-species spatial transcriptomics. Lastly, we have demonstrated the applicability of comparative multi-omics to reveal cellular and molecular innovations in the anterior cingulate cortex during primate evolution.
1. Liu, X., Shen, Q., Zhang, S. Cross-species cell-type assignment of single-cell RNA-seq by a heterogeneous graph neural network. Genome Research, 33: 96-111 (2023).
2. Hao, Y., Zhou, X., Zhao, Q., She, H., ..., Lei, F., Meng, F., Zhang, S., Qu, Y. Avian lung single-cell atlas elucidates evolutionary divergence in endothermic respiration. Molecular Biology and Evolution, 43(5), msag072 (2026).
3. Shen, Q., Zhang, SQ, Zhang, S. Highly accurate reference and method selection for universal cross-dataset cell type annotation with CAMUS. Genome Research, 35: 2527-2538 (2025).
4. Guo, ZH., Huang, DS., Zhang, S. Multi-species integration, alignment and annotation of single-cell RNA-seq data with CAMEX. Nature Communications, 17, 3017 (2026).
5. Zhang, B., Zhang, SQ., Zhang, S. Whole brain alignment of spatial transcriptomics between humans and mice with BrainAlign. Nature Communications, 15, 6302 (2024).
6. Zhang, B., Zhou, X., Zhang, SQ., Zhang, S. Deciphering shared and divergent tissue architectures from cross-species spatial transcriptomics. bioRxiv (2026).
7. Yuan, J., Dong, K., Wu, H., Zeng, X., Liu, X., …, Zhang, S., Su, B. Single-nucleus multi-omics analyses reveal cellular and molecular innovations in the anterior cingulate cortex during primate evolution. Cell Genomics, 4, 100703 (2024).
2. Hao, Y., Zhou, X., Zhao, Q., She, H., ..., Lei, F., Meng, F., Zhang, S., Qu, Y. Avian lung single-cell atlas elucidates evolutionary divergence in endothermic respiration. Molecular Biology and Evolution, 43(5), msag072 (2026).
3. Shen, Q., Zhang, SQ, Zhang, S. Highly accurate reference and method selection for universal cross-dataset cell type annotation with CAMUS. Genome Research, 35: 2527-2538 (2025).
4. Guo, ZH., Huang, DS., Zhang, S. Multi-species integration, alignment and annotation of single-cell RNA-seq data with CAMEX. Nature Communications, 17, 3017 (2026).
5. Zhang, B., Zhang, SQ., Zhang, S. Whole brain alignment of spatial transcriptomics between humans and mice with BrainAlign. Nature Communications, 15, 6302 (2024).
6. Zhang, B., Zhou, X., Zhang, SQ., Zhang, S. Deciphering shared and divergent tissue architectures from cross-species spatial transcriptomics. bioRxiv (2026).
7. Yuan, J., Dong, K., Wu, H., Zeng, X., Liu, X., …, Zhang, S., Su, B. Single-nucleus multi-omics analyses reveal cellular and molecular innovations in the anterior cingulate cortex during primate evolution. Cell Genomics, 4, 100703 (2024).
