[1]  Novel Deep Learning Methods Development，to investigate how the tumor microenvironment influence the progress of solid tumors including glioma and renal cell carcinoma

      We have particular interests in developing novel deep learning methods such as new probabilistic models,  derivative methods, followed by using these new methods to datamine high throughput omics data for investigating the mechanisms associated with tumor microenvironment and progress and tumorigenesis of solid tumors.

[2]  Machine Learning Methods' Applications，to investigate the in-depth interactions between immune cells and progress/tumorigenesis of solid tumors including glioma and renal cell carcinoma

      Machine learning methods are one of the core technologies of artificial intelligence (AI).  We will focus on applying machine learning methods to deep data-mine whole-genome omics data for better understanding of the interactions between immune microenvironments and tumor cells in expecting to develop new tumor vaccines. 

[3]  New Machine Learning Methods' Development, to identify peptide vaccines for tumors and infectious diseases

      We will endeavor to develop brand new machine learning methods for better extracting features and performing pattern recognitions from whole-genome high throughput multiple-omics data to identify peptide vaccines with high immunogenicity for better preventing the occurence of tumors and infectious diseases. 

[4]  Transgenic Animals Construction， to investigate the scientific problems associated with tumor immunology and peptide vaccines

     We will construct transgenic animals to further validate our findings from computational analysis in vivo.

[5]  Diagnostic and Treatment Platform Development

     Based on the computational methods we developed, we will establish an integrative intelligent diagnostic and treatment platform for artificial intelligence personalized medicine purposes.