Research
Our research interests lie in the interdisciplinary fields of computational biology and applied machine learning, with a strong focus on gene regulation. Our lab aims to develop sophisticated bioinformatics and machine learning methods that can be used to build integrative models of transcriptional regulation and interpret non-coding genetic variation from the perspective of gene regulation. We believe that a comprehensive understanding of gene regulation at the genome-wide and system-level is essential for uncovering the causal genetic and molecular mechanisms of disease and aging.
(Click here to see the list of selected publications from our lab)
1. Developing and applying biological foundation model to studying gene regulation.
By leveraging advanced machine learning techniques, we aim to construct comprehensive models that accurately represent the intricate processes involved in transcriptional regulation. These models will integrate various types of biological data, such as genomic, epigenomic, and transcriptomic data, to provide a holistic view of gene regulatory networks. This approach will enable us to identify key regulatory elements and understand their roles in controlling gene expression, ultimately advancing our knowledge of the underlying mechanisms that drive cellular function and development.
2. Investigating the roles of epigenetic regulation in embryonic development, aging and human diseases.
Epigenetic modifications, such as DNA methylation and histone modification, play a crucial role in regulating gene expression without altering the underlying DNA sequence. Our lab aims to uncover how these epigenetic changes influence cellular processes during different stages of life, from embryonic development to aging. By studying these modifications in the context of various diseases, we hope to identify epigenetic markers that could serve as potential diagnostic tools or therapeutic targets. Understanding the interplay between epigenetics and gene regulation will provide valuable insights into the molecular basis of development and disease progression.
3. Developing scalable computational tools for single-cell genomics.
Single-cell technologies have revolutionized our ability to analyze gene expression and genetic variation at the individual cell level, offering unprecedented resolution into cellular heterogeneity. However, the massive amount of data generated by these technologies presents significant computational challenges. Our research aims to create robust and efficient algorithms that can handle the complexity and scale of single-cell data. These tools will facilitate the identification of rare cell populations, the reconstruction of developmental trajectories, and the exploration of cellular dynamics in health and disease. By advancing computational methods for single-cell genomics, we aim to unlock new insights into the fundamental principles of cellular function and diversity.