Bayarbaatar Amgalan

ReDeNovo infers both known and novel mutational signatures from cancer genomes, outperforming existing methods and identifying new signatures in real data.

SpacePhenotyper transfers predictive gene expression-phenotype relationships from bulk data to spatial transcriptomics data to analyze clinical phenotypes in spatial regions of tumors.

TranNet identifies predictors of tumor progression by analyzing gene expression differences between tumor and adjacent normal tissues using TCGA data.

The paper introduces a network-based method to uncover relationships between mutational signatures and biological processes in cancer data.

This study uses mutational signatures to assess the impact of smoking on biological processes related to SARS-CoV-2 infection and disease progression in COVID-19.

RepairSig identifies DNA repair deficiency mutational signatures in cancer by modeling interactions between mutagenic processes, replacing multiple signatures with a single representative one in breast cancer.