Jolene S. Ranek

Multi-omic landscape of human gliomas from diagnosis to treatment and recurrence

This study analyzes multi-omic data from gliomas to identify therapeutic escape mechanisms and TME evolution, finding that N-glycosylation classifies tumor grade and immune transcriptome predicts GBM survival.

Automated classification of cellular expression in multiplexed imaging data with Nimbus

The paper introduces Nimbus, a deep learning model for predicting marker positivity in multiplexed imaging data using a pre-trained dataset, and shows its integration with clustering for cell subtype identification.

The immunometabolic topography of tuberculosis granulomas governs cellular organization and bacterial control

This study identifies hypoxia as a key driver of immune dysfunction in TB granulomas, correlating with bacterial persistence.

Temporal and spatial composition of the tumor microenvironment predicts response to immune checkpoint inhibition

The paper uses spatial imaging and computational analysis to identify features in metastatic tumors that predict response to immune checkpoint inhibition in TNBC patients.

QUICHE reveals structural definitions of anti-tumor responses in triple negative breast cancer

QUICHE identifies low-prevalence cellular niches in spatial datasets, outperforming existing methods, and reveals structural anti-tumor immune responses in TNBC.

The Interplay Between Sketching and Graph Generation Algorithms in Identifying Biologically Cohesive Cell-Populations in Single-Cell Data

The paper investigates how sketching and graph generation methods affect the identification of biologically meaningful cell-populations in single-cell data, finding that sketching is more critical than graph construction.

Feature selection for preserving biological trajectories in single-cell data

This paper introduces DELVE, a feature selection method that improves trajectory inference by identifying dynamically expressed features that better represent biological processes in single-cell data.

Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction

This study benchmarks integration methods for temporal single-cell gene expression data to improve trajectory inference and disease prediction.