Advances of AI methods in single cell spatial omics

Ben Raphael, Professor of Computer Science, Princeton University, is a leading researcher in computational genomics and cancer genomics, with a strong focus on spatial transcriptomics and tumor heterogeneity. His work has pioneered algorithmic methods for analyzing single-cell and spatial omics data, particularly in understanding cellular organization in cancer. His research has led to widely used computational tools for inferring spatial domains, tumor evolution, and network/pathway analysis of genetic variants.

Theodore Alexandrov, Assistant Professor at the Department of Pharmacology, UCSD, is a global expert in spatial metabolomics and computational mass spectrometry. He has developed machine learning methods to analyze spatial omics data, particularly for metabolite localization and tissue microenvironment profiling, with applications in drug discovery & therapy development.

Xuegong Zhang, Professor at Tsinghua University, is a pioneering researcher in computational biology and bioinformatics. His work focuses on developing AI-driven algorithms including foundation models to improve the analysis and interpretation of single cell multiomics data and spatially resolved gene expression data. His contributions have significantly advanced cell type identification, spatial domain detection, and embedding learning of cells. Xuegong currently serves as the vice-president of the International Society for Computational Biology (ISCB).