Group Introduction


           Peking University Biomedical Informatics Department Li-Tingting Group belongs to PKU school of Basic Medical Science. Our group dedicates in regulatory networks of protein post-translational modification and integrative mining of various high-throughput data. The mission of us is to pursue excellence in scientific research and education on fundamental questions in life science with informatics and systems approaches, and on the informatics mechanism of life system. Our laboratory locates in PKU Health Science Center campus. Besides sharing resource of the High-performance Computing Platform of Peking University, we possess independent server cluster and high-performance image processor, which means we could conduct deep-learning, big data analyzing and data mining conveniently.

     Tutor's Introduction


           Li Tingting. She graduated from Tsinghua University with Ph.D. of bioinformatics. Now, she is an associate professor of Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center.
           Doctor Li has been in charge of the National Key Research and Development Plan. As the principle investigator, she has undertaken/completed 2 projects of the National Natural Science Foundation of China and 1 youth project, participated in 2 national projects including the 973 program, and was selected as a “Young Talents Project” of Beijing Higher Education Institution. In the research field of bioinformatics, a total of 32 SCI papers were published. Among them, 20 articles published in the journals of Nucleic Acids Research, Mol Cell Proteomics, Briefings in Bioinformatics and so on, where Professor Li was named as the first author or corresponding author.
           Research interest: (1) The function and evolution of post-translational regulatory networks; (2) Integrative mining of various high-throughput data.

     Group Research Interest


           1. Prediction of protein post-translational modification sites

           2. Prediction and network construction of PTM sites cross-talk

           3. Cancer proteomics

           4. Integrative mining of mass spectrum data, next-generation sequencing data and other omics data

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