Hatice U. Osmanbeyoglu

DGAT imputes spatial protein expression from transcriptomics-only data, revealing cell states and tissue organization not visible from RNA alone.

STAN infers spatially informed transcription factor activity using spatial transcriptomics data from lymph node, breast cancer, and glioblastoma datasets.

The paper shows that loss of CFIm activates YAP/TAZ and links mRNA cleavage and polyadenylation inhibition to BRCAness through DNA repair gene repression.

This study identifies NuRD complex downregulation and altered proteostasis in quiescent ovarian cancer cells and shows that targeting these mechanisms can synergistically kill cancer cells.

The paper identifies S100A11 as a factor promoting macrophage recruitment in ER+ breast cancer through single-cell RNA-sequencing analysis and functional experiments.

This study uses spatial analysis to examine immune cell distribution in mesothelioma tumors and its association with gene expression and patient outcomes.

The paper develops a database linking cell surface proteins and transcription factors in immune cells using CITE-seq data from COVID-19 patients and healthy controls.

The paper identifies BAP1 loss as a predictor of immunogenicity and potential response to PD-1 therapy in malignant pleural mesothelioma.

The paper introduces CITRUS, a deep learning model that infers transcriptional programs influenced by somatic alterations across 17 cancer types using genomic, transcriptomic, and epigenomic data from The Cancer Genome Atlas.

SPaRTAN links cell-surface receptors to transcription factors using CITE-seq data and cis-regulatory information, revealing novel receptor-TF relationships in immune and tumor cells.

The paper develops PSIONIC, a machine learning method that integrates chromatin accessibility and RNA-seq data to infer transcriptional programs in gynecologic and basal breast cancers, revealing regulatory differences and survival associations.