Asaf Pinhasi

CloneTrast uses contrastive learning to infer T-cell clonal niches from single-cell RNA-seq, identifying NK-like niches linked to poor immunotherapy response across cancers.

The paper introduces CORAL, a scalable framework to infer mutation rates and spectra across species, revealing variation linked to life-history traits and identifying evolutionary mutational signatures.

This study creates a single-cell atlas of breast cancer microenvironments, identifying subtype-specific tumor-immune interactions and therapeutic vulnerabilities.

This study analyzes T cell clones in cancer patients to identify transcriptional differences linked to response to checkpoint immunotherapy.

The paper develops a machine learning pipeline to predict immune checkpoint inhibitor response using single-cell RNA-seq data, identifying an 11-gene signature and cell-based predictors with high accuracy across multiple cancer types.