Computational Method Development
Applying machine learning and deep learning techniques to clinical data for disease prediction, diagnosis, and treatment optimization.
Projects
Microsatellite Instability Detection using DNA
Comprehensive microsatellite instability (MSI) analysis across various cancer types to identify potential biomarkers for precision oncology and personalized treatment strategies.
Collaborators: Harvard Medical School
Related Publication:
A molecular portrait of microsatellite instability across multiple cancers
Microsatellite Instability Detection using RNA
MIRACLE: Microsatellite instability (MSI) detection using RNA-seq data, enabling MSI status determination without DNA sequencing.
Related Publication:
Detecting microsatellite instability by length comparison of microsatellites in the 3′ untranslated region with RNA-seq
Microsatellite Instability Detection using WSI
Deep learning-based MSI detection from whole slide images, enabling cost-effective screening across multiple cancer types without molecular testing.
Collaborators: Seoul National University Hospital
Related Publication:
Multi-cancer analysis of histopathologic MSI screening based on digital histology image
NGS-based Sample Identity Verification
NGS-based sample identity verification tool for human samples to ensure data integrity and prevent sample mix-ups in genomic studies.
Collaborators: Harvard Medical School, Samsung Medical Center
Related Publication:
NGSCheckMate: software for validating sample identity in next-generation sequencing studies within and across data types