Our lab is interested in leveraging OMICS, electronic medical records, and advanced machine learning methods to identify new therapeutics and biomarkers ( B. Chen, 2016, B. Chen, 2020). Our three main research areas are: 1) OMICS-based drug discovery, 2) OMICS-based biomarker and target discovery, and 3) virtual platform development to support translational research. The data we primarily use include bulk/single-cell transcriptomics, spatial transcriptomics, and EMR/EHR. In addition to traditional bioinformatics methods (e.g., sequence analysis), we actively develop and implement new machine learning models such as transfer learning and deep reinforcement learning to deal with the emerging big data. To achieve the goal of identifying new therapeutics, we closely collaborate with data mining experts, statisticians, bench scientists, medicinal chemists, and physicians.