Eytan Ruppin

This paper introduces Sgootr, a method to infer tumor lineage trees from single-cell methylation data, revealing metastatic progression models and identifying lineage-informative CpG sites.

This study identifies a decoupling signature that reduces the correlation between cytotoxic and exhausted T cell activities, improving the prediction of immunotherapy response in melanoma.

ENLIGHT predicts patient response to targeted and immunotherapies using tumor transcriptomes without prior treatment response data, showing better accuracy than existing transcriptomics-based signatures.

CAMMiQ improves microbial strain-level detection and quantification in single-cell metagenomics by using combinatorial optimization and doubly-unique substrings, outperforming existing methods in accuracy.

The paper predicts anti-SARS-CoV-2 targets by identifying synthetic lethal partners of host genes altered during infection, validated through CRISPR-Cas9 screens and siRNA experiments.

This paper introduces PERCEPTION, a framework using single-cell transcriptomics to predict cancer treatment response and resistance, outperforming bulk expression-based methods in clinical trials.

The paper uses genome-scale metabolic modeling to identify SARS-CoV-2-induced metabolic changes and predict antiviral targets validated through drug and genetic screens.

The paper analyzes pan-cancer single-cell transcriptomes to evaluate and identify new CAR targets for head and neck cancer, finding five promising targets with superior selectivity and safety.

The paper identifies a transcriptionally distinct subpopulation of healthy acinar cells with features of pancreatic progenitors and PDAC, linked to poor patient survival and age-related changes.

This study predicts tissue-specific gene expression from whole blood transcriptome using GTEx data, finding that blood expression can predict expression in many tissues and improve disease prediction.

The paper develops CODEFACS and LIRICS to deconvolve bulk gene expression and identify clinically relevant ligand-receptor interactions in tumors, finding specific T-cell interactions linked to anti-PD1 response.

The paper uses single-cell transcriptomics data to explore optimal combination therapies for cancer by analyzing receptor targets across multiple tumor types.

The paper introduces a functional genomic complexity (FGC) score to assess genomic heterogeneity in liver cancer, finding higher FGC correlates with worse prognosis and immune evasion.

This study compares proliferation and migration transcriptomic signatures in breast cancer, finding migration is more strongly linked to patient survival.

The paper identifies 71,946 genetic interactions in cancer using a novel method, revealing non-synthetic-lethal interactions that explain driver gene specificity and drug response.

The paper compares genomic features between African American and European American lung cancers, finding higher genomic instability and homologous recombination-deficiency in African American tumors.