The major interest of our lab is mechanistic understanding of crop trait development and cancer progression, using the cutting edge of omics (such as single cell genomics, transcriptomics and regulomics) technologies and AI-based (statistical and machine learning) methods. The research advances will, in turn, provide greater impetus for translation of genomics to the field or to the clinic.
The genome encodes a complex and evolutionary diverse regulatory grammar in terms of sequences that forms the basis for most life on earth. We devote to investigate the mechanistic basis of plant organ development and human disease progression, using advanced multi-omics and computational modelling techniques. Our efforts are focused on several aspects:
to develop machine/deep learning-based approaches to study the sequence determinant or grammar of spatiotemporal specificity using big ‘omics’ data.
- Regulatory Genomics
to investigate molecular mechanisms and genetic variation of cis-regulatory elements (e.g., enhancers) underlying organ development or cancer progression.
- Single Cell Genomics
to develop and to apply intelligent tools for fine mapping cellular architectures and spatiotemporal regulation dynamics in complex normal and tumor tissues.
- Translational Genomics
to harness big ‘omics’ data, comparative genomics and AI for precision breeding and medicine.