Roded Sharan

The paper introduces RePrint, a method to infer shared DNA repair mechanisms between mutational signatures, outperforming existing methods and enabling guilt-by-association predictions.

The paper introduces MIRAGE, a multi-modal generative model that integrates protein sequence, interaction, and localization data to improve function prediction and module detection.

The paper develops a framework to identify transcription factors mediating signaling convergence from multiple receptors during cell-cell interactions using single-cell RNA-seq data.

This paper introduces mutation-level covariate models to analyze mutational signatures and shows they better capture replication strand specificity than standard models.

The paper systematically analyzes proteomic and genomic data of SARS-CoV-2 variants to identify how mutations affect viral gene expression, host interactions, and immune evasion.

The paper develops a method to deconvolve cell-type mixtures using enhancer-promoter methylation profiles from long-read data, achieving higher accuracy than promoter-based approaches.

The paper identifies essential genes and fluconazole resistance genes in Candida glabrata using Hermes transposon insertion profiling and machine learning.

The paper compares transposon mutagenesis methods in yeast to infer gene essentiality, finding biases in insertion sites and recommending transposon choices for accurate essentiality prediction.

The paper identifies functional pathways associated with mutational signatures in breast cancer, revealing differences in the etiology of APOBEC and clock-like signatures.

This paper introduces sequence-dependent models for mutation signatures, improving the resolution of mutation signature activity in cancer by accounting for dependencies between mutations.