Masroor Bayati

The paper identifies prognostic genomic interactions (PGIs) across multiple cancer types by analyzing pairwise genomic alterations and their association with patient outcomes.

The paper identifies mutational signatures in cancer genomes that alter protein sequence motifs in signaling networks, linking mutation etiology to functional consequences.

The paper identifies immunogenomic interactions across 13 cancer types using a multi-omics framework, linking genomic alterations and immune features to patient survival.

The paper introduces DPM, a directional integration method for multi-omics data that identifies consistent gene and pathway changes across datasets and highlights inconsistent evidence.

CANCERSIGN identifies and classifies mutational signatures in cancer genomes using raw mutation data, enabling clustering and analysis of ICGC datasets to discover novel signatures.