Innovative advances in various experimental techniques, such as DNA sequencing and genome editing, have made it possible to obtain high-throughput life science data. Our goal is to understand biological phenomena by applying information and mathematical theories to such large-scale data, and to contribute to regenerative medicine and personalized medicine.

We have developed novel algorithms for single-cell RNA sequencing data to understand

• cellular differentiation process and expression dynamics
• regulatory network
• unannotated differentially expressed transcripts.

We have analyzed Parkinson’s disease specific miRNA sets to identify disease-associated metabolic pathway and understand disease mechanism.