Yuanfang Guan
Professor of Computational Medicine and Bioinformatics and Internal Medicine
Computational Medicine and Bioinformatics, Michigan Medicine
Internal Medicine, Nephrology
Associate Professor of Computational Medicine and Bioinformatics and Associate Professor of Internal Medicine, Medical School
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.
