Prof. CHEN Lingxi (陳凌曦)

PhD (CityU)

Visiting address
TYB-Room 1B-109, Block 1
Phone: +852 34422714

Author IDs


Prof. Chen is a highly motivated researcher who holds a BSc in Computer Science from the University of Nottingham, an MSc in Web Science and Big Data Analytics from University College London, and a PhD in Computer Science from City University of Hong Kong. Her research area is computational biology, and she has published high-impact SCI papers in journals such as Nature Communications, Nucleic Acids Research, The Lancet Digital Health, etc. In Dec 2023, she joined the Department of Biomedical Sciences as an assistant professor. She is passionate about applying computer science and artificial intelligence to solve critical challenges in biomedical fields, such as cancer, complex diseases, and animal health.

Research Interests/Areas

Prof. Chen is dedicated to advancing research at the intersection of computer science and biomedicine, with a primary emphasis on developing computational tools and platforms that can address critical issues in cancer, complex diseases, and animal health. Her research interests span four distinct domains: Genomic Structural Aberrations, Single-Cell and Spatial Omics, AI in Healthcare, and Online Biomedical Platforms. These areas are essential for advancing computational biology and its contribution to the field of biomedical sciences.

  1. Genomic Structural Aberrations
    To develop specialized tools for the detection and analysis of genomic aberrations, such as single nucleotide variations (SNVs), copy number variations (CNVs), and complex structural variations (CSVs). To identify the aberration biomarkers associated with multiple cancers and complex diseases for precision medicine.
  2. Single-Cell and Spatial Omics
    To design computational algorithms tailored for the analysis of bulk, single-cell, and spatial omics data. To address the challenges such as imputing missing data, dimension reduction, clustering, data integration, and more.
  3. AI in Healthcare
    To build diagnostic and prognostic AI models for various conditions, including cancer and complex diseases in humans and animals.
  4. Online Biomedical Platforms
    To create user-friendly online webservers and databases that offer integrated analysis pipelines and interactive visualization interfaces. To enable non-bioinformaticians to explore complex biological data with simple mouse operations.


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