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We are a statistical group with major applications on genomics and bioinformatics. We mainly focus on data mining of high-throughput genomic and proteomic data and methods for
biomarker detection including supervised (classification) and unsupervised (clustering) machine
learning and detection of differentially expressed genes. Currently we are especially focused on
information integration of multiple genomic studies and improving robustness and portability of prediction
models for translational research. Related research also include
statistical modeling, statistical computing and graphical visualization of data. Collaboration
with biology labs plays an important role where most of our projects and methodological ideas
come from.
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