Katayoun Rezvani

This paper introduces ABMACT, an agent-based model to simulate tumor-immune dynamics in adoptive cell therapy and identifies key factors for optimal efficacy.

The paper constructs an immune cell knowledge graph (ICKG) using AI to annotate immunological gene sets more accurately than existing methods.

The paper constructs immune-specific gene sets using non-negative matrix factorization on bulk RNA-seq data to improve immune subtype analysis and ICB response prediction.

The paper develops METAFlux to infer metabolic fluxes from bulk and single-cell RNA-seq data, showing improved accuracy over existing methods in cancer TME analysis.

bindSC integrates single-cell multi-omics data accurately by aligning rows and columns without approximations, outperforming existing methods in various biological contexts.