Assistant Professor, Computational Medicine and Bioinformatics, Michigan Medicine
Internal Medicine, Nephrology
Functional genomic data, such as RNA-seq, microarray, protein-protein interactions, are growing exponentially these days. The research in Guan Lab focuses on developing novel and high-accuracy algorithms that integrate these data for predicting gene functions and networks. We have the following ongoing projects in the lab:
1. Modeling dynamic networks: Biological networks may rewire during cell lineage differentiation, tissue development or a disease course. We are actively developing novel algorithms that capture such dynamics. The algorithm contributed by our group (The GuanLab Team) was one of the six best-performance methods (out of over 100 submissions) in the HPN-DREAM 2013 Competition. We achieved the best performance (along another team) to subchallenge 2A, as well as the best aggregate prediction to subchallenges 2A and 2B: network timecourse prediction.
2. Isoform-level analysis: We are exploring algorithms that allow us to go beyond the traditional gene-level analysis to the isoform level.