Our lab works on computational proteomics of protein turnover and protein networks. We develop and implement bioinformatics and statistical approaches for biological inferences from large-scale, high-throughput proteomics data and their applications
in biomedical problems. We developed algorithms for peak detection and quantification, identification
of structures in multivariate data, stochastic time-course modeling to extract dynamical features, construction of
protein networks, and error control in the resulting inferences. In collaboration with our experimentalist colleagues,
we applied these techniques to study molecular mechanisms in non-alcoholic fatty liver disease, aging, and neurodegenerative diseases.