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.

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.