Controlling cancer requires comprehensive understanding of the molecular, cellular, and organizational properties of tumor tissue. While clinical pathology has served as [...]

Molecular phenotypes, or subtypes, can describe cancer as distinct diseases within primary tissues-of-origin. Machine learning (ML) can be applied to this molecular taxon [...]

Physical and functional partitioning of the biological membrane have been implicated in regulating heterotypic and homotypic interactions of proteins on the membrane. Amo [...]

A multi-module diabetic retinopathy diagnostic system based on structural and angiographic optical coherence tomography using deep learning.

This thesis presents a suite of deep learning frameworks designed to address key challenges in the analysis of Electron Microscopy (EM) images of cancer cells, which are [...]

In order to improve treatment for a variety of forms of cancer, improved basic understanding of tumor microenvironments is needed. In this thesis, I review the state of t [...]

We demonstrate that the proportions of different functional tissue units (FTUs) within prostate tissue constitute a simple, biologically interpretable, and effective whol [...]

This dissertation presents a structure-informed deep learning framework for predicting protein ubiquitination sites using AlphaFold2-derived 3D structural features and gr [...]

The aim of this research is to utilize MRF techniques to characterize vascularization differences between healthy pancreas tissue and tumorous tissue. To do this, both he [...]

This dissertation develops physiology-guided machine learning digital twins and a person-centered decision support system to improve personalized diabetes management. By [...]