J. O. Deasy

The paper maps the differentiation landscape of osteosarcoma using single-cell data to identify gene modules linked to cell fate and patient survival.

The paper identifies six transcriptomic archetypes from normal tissues that correlate with cancer patient survival and drug sensitivity, suggesting cancer adopts normal tissue transcriptional features.

This study uses gene interaction network analysis in multiple myeloma to identify immune-related genes associated with shorter survival.

This paper introduces a network curvature-based method to predict prognosis in high-grade serous ovarian cancer patients treated with immune checkpoint inhibitors using copy number alterations.

This paper demonstrates the feasibility of creating 3D printed CT phantoms from patient data to assess the repeatability and robustness of radiomic features across different scanning parameters.

The paper reclassifies breast tumors into 7 classes using interpretable mRNA signatures, showing improved survival outcomes for myoepithelial and immune-related phenotypes.