Comparative study of nature-derived FDA approved drugs and Traditional Chinese Medicine (TCM) to reveal the mechanism of TCM. The mechanism of majority of the TCM is still unclear, this study tries to conduct a comparison between FDA approved drugs and TCM on the phylogenetic perspective to have an understanding on the distribution pattern between these two drug systems, which is expected to give us a deep understanding of the mechanism of TCM.
Deriving stable microarray cancer-differentiating signatures by machine learning and feature-elimination methods, and evaluating consensus scoring of multiple random sampling and Gene-Ranking’s consistency. Signatures identified reflect disease mechanism, and can provide indicators for disease diagnosis. My current interest lies in identifying biomarkers for breast cancer and major depression.
Identifying next generation innovative therapeutic targets for specific disease types, such as Obesity, Major Depression, Cancer, and so on. Collective methods are applied, which include: A. genetic sequence similarity analysis between drug-binding domains; B. computation of number of human similarity proteins, number of affiliated human pathways, and number of human tissues of a target; C. structural comparison between drug-binding domain; D. target classification based on physicochemical characteristics detected by machine learning.
Led and conduct the development of bioinformatics databases, which collect information of Biology, Pharmacy, Chemistry and so on. Moreover, we are interested in constructing innovative software for drug discovery and bioinformatics, which involves design and implementation of an integrated bioinformatics software system for novel therapeutic target agent explorations.
Conducting biostatistics study on the distribution of molecules with therapeutic effect, especially drugs approved and in clinical trial, across all biological species, and identifying key species for ecological protection. Comprehensive biostatistics studies on therapeutic targets in clinical trial, and comparative analysis against targets with drugs approved. Studying correlating groups of genes by utilizing graph theory for filtering complex gene correlation network. Genetic variation identified indicate complex inter- and intra-individual differences.