Elana Judith Fertig

SEngEAR is a web platform for annotating senescent cells, visualizing communication networks, and comparing senescence signatures across datasets and contexts.

The paper identifies 17 consensus transcriptomic programs in ccRCC using NMF and links them to cellular sources and clinical outcomes, revealing converging trajectories toward aggressive disease.

This study uses spatial transcriptomics to analyze fallopian tube lesions and identifies immune and stromal changes associated with early ovarian cancer development.

This study identifies distinct senescent cell populations in human fibroids and shows their role in ECM remodeling and vascular dysfunction through single-cell RNA sequencing and functional analysis.

The paper identifies periductal fibroblast density as a key factor in immune exclusion in pancreatic cancer, showing that CD44-dependent stromal checkpoints restrict leukocyte access to tumor epithelium.

The paper calibrates a spatial QSP model using CODEX data to predict TME states and identify biomarkers for HCC immunotherapy response.

CODAvision provides a user-friendly interface for rapid, customizable medical image segmentation using deep learning, enabling non-experts to train and evaluate models across diverse imaging modalities.

The paper identifies distinct fibroblast and perivascular senescence subtypes in fibrosis using single-cell and spatial transcriptomics, showing their spatial organization and roles in fibrosis progression.

PICTographPlus infers clone-resolved transcriptional programs from bulk RNA-seq by integrating clonal phylogenies and proportions, achieving high accuracy in recovering clone-level expression and localizing pathway changes.

This study shows antigen-presenting cancer-associated fibroblasts (apCAFs) in murine pancreatic tumors influence regulatory T cell phenotype and function through CXCL9 and CCL22, affecting immune checkpoint blockade response.

This study shows that γδ17 T cells influence fibrosis by altering ECM composition and vascularity through interactions with stromal cells.

This paper introduces a trajectory-centric framework to model dynamic tumor-immune interactions, showing that treatment-phase TME states predict immunotherapy response better than pre-treatment states.

STAPLE automates spatial transcriptomics analysis by integrating multiple tools into a modular framework and provides AI-driven interpretation of results.

The paper develops a 3D multi-omic pipeline using deep learning to analyze chemotherapy response in pancreatic cancer, identifying molecular signatures of resistance and fibroblast-cancer interactions.

The paper introduces dominoSignal, a software tool that statistically tests differential cell signaling from single-cell data to infer changes in cell-cell communication networks.

This study shows immune checkpoint inhibitors enhance type 2 immunity and regeneration in ECM-treated wounds by promoting Treg/TH2 plasticity and pro-regenerative macrophages.

The paper identifies Treg clonal expansion and immunosuppressive functions in fibrosis induced by synthetic implants, showing their role in modulating stromal cell behavior.

This study identifies a gene signature of metastatic potential by linking pre-metastatic states to metastatic outcomes in mouse pancreatic cancer and validates its relevance in human tumors.

The paper introduces an algorithm to correct mislabeling and align object positions in longitudinal 3D cell culture imaging, improving data integrity and reproducibility.

CycleGRN infers gene regulatory networks from single-cell RNA-seq data by modeling cell cycle dynamics as a stochastic differential equation, achieving strong performance on synthetic and biological datasets.

The paper improves gene regulatory network inference by integrating cell cycle positions into oscillatory network analysis, showing better performance for early progenitor cells.

The paper identifies multi-omic phenotypes in pancreatic precursor lesions to distinguish progressive from indolent precancers using integrative transcriptomic, genomic, and spatial data.

The paper introduces a framework for mathematical modeling of multicellular systems using interpretable cell behavior rules derived from biological knowledge and multi-omics data.

PIVOT is an open-source tool for nonlinear registration of multi-omic spatial data, demonstrating improved throughput compared to existing workflows.

The paper investigates how cancer-associated fibroblasts (CAF) influence tumor cell behavior in pancreatic ductal adenocarcinoma by driving EMT and altering cell subtypes, with IL-8 playing a key modulatory role.

The paper uses virtual patient models to evaluate biomarkers for predicting response to PD-1 blockade in TNBC, finding that early on-treatment biomarkers and blood-based markers offer better performance.

This study uses spatial multi-omics to identify TLS-associated immune niches in PDAC that correlate with improved survival in immunotherapy responders.

The paper introduces computational tools for NMF analysis of single-cell RNA-seq data, demonstrating their use in quantifying cell state transitions in pancreatic cancer.

scANNA is an interpretable deep learning model for scRNA-seq that uses neural attention to learn gene associations, enabling downstream analyses without retraining.

This paper introduces SpaceMarkers, a method to infer molecular changes from cell-cell interactions in spatial transcriptomics data, applied to tumor-immune interactions in metastasis and immunotherapy.