Welcome to Kuang Lab!
Our lab is interested in developing general machine learning models and algorithms for integrative mining of large-scale transcriptomics data and biomedical knowledge bases to understand the molecular characteristics of biological functions and phenotypes. my lab developed new methods for 1) phenome-genome association analysis by mining knowledge graphs and 2) phenotype prediction and biomarker identification from gene expression profiling data using network-guided machine learning methods. In particular, we develop high-order relational learning and meta-analysis methods for integrative studies of multiple knowledge graphs, and single-cell and spatially resolved transcriptomic data. Our lab is affiliated with the Computer Science and Engineering Department and the Bioinformatics and Computational Biology Program.
