Ruppin Lab

AI driven breast cancer subtyping and risk stratification from slide-inferred pathologist-interpretable nuclear features

Software for analyzing the data in the trialtrove database

EXPRESSO is a drug-specific supervised LASSO logistic regression framework for transcriptome-based treatment response prediction. It evaluates predictive power of drug targets and biomarkers across patient cohorts using a Leave-One-Cohort-Out Cross-Validation (LOCO-CV) strategy.

Investigate the predictive power of the TME for patient response in a cell-type-specific manner

Kraken2 + Bracken based pipeline for classifying and quantifying microbial reads from GDC TCGA WGS data

Code for the identification step for CSI-Microbes

Predictive models and analysis of cancer prognosis and drug response using primary tumor microbial abundances derived from WGS and RNA-seq sequencing data for 32 TCGA cancers (Poore et al. Nature 2020), including equivalent models using TCGA RNA-seq gene expression and combined microbial abundance and gene expression for comparison.

The code for running the analysis component of CSI-Microbe