Welcome to the Kuang Lab for Computational Biology. Our research focuses on understanding biomolecular and cellular systems through machine learning, network analysis, and statistical modeling. We are particularly interested in uncovering latent and high-order structures in spatial and single-cell transcriptomics data, as well as in biological networks, to better characterize the molecular basis of biological functions and phenotypes.
We develop novel computational methods for learning spatial patterns from spatial genomics data, performing phenome–genome association analysis through knowledge graph mining, and predicting phenotypes and identifying biomarkers from gene expression data using network-guided machine learning approaches. In addition, our lab also worked on string kernel and label propagation techniques for protein remote homology detection, providing insights into protein structure and function.
The lab (6-225D @ Keller Hall) is affiliated with the Department of Computer Science and Engineering and the Bioinformatics and Computational Biology Program at the University of Minnesota Twin Cities.