Jung Hun Oh

NetFlow constructs graph representations of biomedical data to reveal structured patterns and refine clinical subtypes in cancer datasets.

ORCO is an unsupervised network-based tool that computes Ollivier-Ricci curvature to analyze biological network robustness and identify therapeutic targets.

This study identifies high WEE1 expression as a prognostic marker in multiple myeloma, showing it is independently linked to poor survival and associated with dysregulated P53 pathways.

This paper introduces a cloud-based framework for AI medical image analysis using pyCERR and Cancer Genomics Cloud, enabling end-to-end workflows without local software installation.

STRATIS provides cloud-enabled software workflows for AI modeling in radiotherapy and imaging analysis, supporting multi-institutional collaboration and deployment.

The paper develops an NLP/ML model to identify DILI-related literature using titles and abstracts, achieving high accuracy across multiple validation sets.

This study uses geometric network analysis of RNA-Seq data to cluster pediatric sarcoma tumors and identify therapeutic targets through unsupervised methods.

The paper introduces vWCluster, a network-based clustering method for multi-omics breast cancer data using vector-valued optimal transport, identifying tumor subtypes with distinct survival rates and immune microenvironment profiles.

The paper presents an open-source library of deep-learning models for image segmentation and outcomes in oncology, enabling validation, automation, and integration into clinical workflows.

This study introduces a network-based method combining optimal mass transport and machine learning to predict drug sensitivity in cancer cell lines by clustering genomic and chemical features.

This paper introduces iWCluster, a network-based multiomics clustering method for breast cancer data, which recovers known subtypes and identifies hypoxia-related genes associated with poor prognosis.