Louis Joseph Vaickus

XpressO-Lung predicts spatial gene expression patterns in lung adenocarcinoma using histopathology images, validated against immunohistochemistry, showing strong concordance with known morphologic features.

X-SPATIO predicts and visualizes spatially resolved biomarker expression in TNBC by linking H&E morphology with spatial transcriptomic and proteomic data.

TRACE co-registers elemental imaging and histopathology to enable spatial analysis of tissue sections through a web application.

This paper develops a deep learning algorithm for 3D-HDAM to detect dyssynergia, showing comparable accuracy to traditional and hybrid models but better handling of ambiguous cases.

The paper presents a modular pipeline for federated health data analysis using deep learning to predict health outcomes across diseases.

The paper introduces MethylCapsNet and MethylSPWNet, deep learning models that organize DNA methylation data into biologically relevant capsules to improve interpretability in classifying CNS tumors.

The paper introduces GCN4R, an R library for fitting graph neural networks on networks to generate embeddings for network-based tasks like community detection.