Computational Gastroenterology
Our research encompasses single-cell and spatial ‘omics,’ computer vision (e.g. deep learning and AI-driven modeling) and systems biology. We employ advanced computational biology and machine learning techniques to derive actionable insights from multi-omics datasets and digital pathology, drawing from both experimental models and patient samples. Additionally, we leverage multimodal deep learning approaches to analyze clinical imaging, EMR, and informatics at the patient level. By integrating these diverse data sources, we aim to drive advancements in precision medicine and deepen our understanding of complex biological systems.
- Erica DePasquale (Bioinformatics, Single-cell Sequencing, Software Development)
- Jasbir Dhaliwal (IBD, AI, Computational Pathology, ML, Informatics)
- Yael Haberman (Microbiome, Crohn's Disease, Ulcerative colitis, Metabolism, Mucosal Repair)
- Sean Moore (Undernutrition, Enteropathy, Global Health, Immunity)
- Anna Peters (Liver Transplant, Rejection, Organoids, Transcriptomics, Immunosuppression)
- Yunguan Wang (Bioinformatics, Genomics, Spatial Omics, Autoimmune Liver Disease)
