Enakshi Saha

SIREN estimates individual-specific gene regulatory networks from multi-omics data, revealing epigenetic heterogeneity in lung adenocarcinoma that correlates with patient survival.

The paper identifies sex-biased regulation of cancer genes in normal tissues, linking these differences to sex-specific cancer incidence patterns.

The paper identifies sex-specific gene regulatory changes in thyroid aging that contribute to sex differences in thyroid disorder risk.

This study identifies aging-related changes in gene regulatory networks linked to LUAD risk, prognosis, and therapy response using multi-omics data from GTEx and TCGA.

The paper identifies sex differences in lung adenocarcinoma by integrating multi-omics data to discover sex-biased signaling pathways and potential sex-specific therapies.

The paper develops a Nextflow workflow for reproducibly inferring regulatory networks from TCGA data, showing its application to colon cancer subtypes.

This study investigates how loss of the Y chromosome in lung adenocarcinoma affects the tumor immune environment and response to immunotherapy.

BONOBO infers sample-specific co-expression networks to capture individual heterogeneity in gene regulation, outperforming existing methods in yeast, breast cancer, and thyroid tissue analyses.

The paper identifies sex differences in gene regulatory networks in lung adenocarcinoma and identifies potential sex-biased cancer therapeutics.

This paper introduces node2vec2rank, a method for graph differential analysis that identifies biologically relevant differences between weighted networks in cancer subtypes and single-cell dynamics.