Introduction
The AI in Genomics Lab is a cross-disciplinary research group which aims to transcend the disciplinary boundary of genomics. We integrate data science, machine learning, and biology approaches to decipher disease mechanisms by finding cell morphologies and molecular profiles associated with disease progression. Such knowledge is demanded for precision medicine and the therapeutic development of small molecules and their delivery to target regions.
This research lab is directed by Dr. Jian Hu, Assistant Professor of Human Genetics at Emory School of Medicine.
Research
Developing machine learning methods for spatial multi-omics integration
Multi-modal spatial omics represents a paradigm shift in understanding complex biological systems by integrating diverse omics modalities within their native tissue contexts. Different spatial omics techniques capture distinct molecular information, including transcriptomics, proteomics, metabolomics, chromatin accessibility, and histone modifications. Together, these modalities provide a comprehensive view of cellular and tissue functions, which is crucial for unraveling the molecular mechanisms of diseases. One focus of my lab is to develop state-of-the-art machine learning analytical tools to help researchers better mine the rich information in spatial multi-omics data.
Developing statistical tools for single-cell RNA sequencing analysis
The advent and rapid development of single-cell technologies have made it possible to study cellular heterogeneity at an unprecedented resolution and scale. Cellular heterogeneity underlies phenotypic differences among individuals, and studying cellular heterogeneity is an important step toward our understanding of disease molecular mechanisms. Single-cell technologies offer opportunities to characterize cellular heterogeneity from different angles, but linking cellular heterogeneity with disease phenotypes requires careful computational analysis.
Developing AI-driven analytical tools for digital pathology imaging data
The adoption of digital pathology has enabled the curation of large repositories of gigapixel whole-slide images (WSIs), which are invaluable for examining cellular morphology and its changes during embryonic development or disease progression. One focus of my lab is to develop label-free, AI-driven methods for medical imaging data analysis. These tools can be easily applied to studies with limited training samples, eliminating the need for cumbersome labeling steps.
News
10/18/2025: Trainee Research Excellence Recognized at ASHG 2025
We are thrilled to announce that Chenyang Yuan has been selected as one of the winners of the Predoctoral Trainee Research Excellence Award, and Shihan Liu has been recognized as …
10/17/2025: AI in Genomics Lab attended ASHG 2025
Oral talks presented by lab members: Chenyang Yuan: mcDETECT: Characterizing mRNA Localization in Polarized Neuronal Compartments with Spatial Transcriptomics. Shihan Liu: BRIDGE: Inferring 3D Molecular Tissue Structure from Sparse Spatial …
10/1/2025: Dr. Hu Receives 2025 NACC New Investigator Award
Dr. Hu, has been selected as a recipient of the 2025 National Alzheimer’s Coordinating Center (NACC) New Investigator Award. This highly competitive award provides funding to support innovative research in …