I started my PhD in Michigan Tech. in August 2015 after working as a lecturer for ten years in Northeast Forestry University, China.
My PhD research is focused on developing appropriate statistical and machine learning models to reconstruct gene regulatory network from gene expression data. In these 3 years, I developed a backward elimination random forest (BWERF) algorithm for reconstructing multilayered hierarchical gene regulatory network, and a new graphical Gaussian model (JRmGRN) for joint reconstruction of multiple gene regulatory networks using data from multiple tissues or conditions. Reconstructing gene regulatory networks helps elucidating the nature of complex biological processes and disease mechanisms in a variety of organisms. Comprehending the associations between genes has important ramifications in pathological studies for explaining disease pathways and identifying biomarkers for prognosis and diagnosis.
I would like to give my sincere gratitude to the Graduate School at Michigan Tech. for this financial support, which gives me an opportunity to put all my efforts on completing my PHD degree this semester.