Megha Padi

PARROT detects change-points in dynamic gene regulatory networks using Stochastic Block Models, identifying module reassignments and connection changes in human and mouse data.

This study uses probabilistic network modeling to identify RBPJ as a key regulator of stemness in Merkel cell carcinoma, showing its potential as a therapeutic target.

The paper shows that transient centrosome loss in prostate cells causes chromosomal instability, leading to oncogenic changes linked to poor clinical outcomes.

The paper analyzes the transcriptional network of HCMV in latency by comparing gene expression with and without UL135 and UL138, revealing their roles in viral gene regulation and hematopoietic differentiation.

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

This paper introduces TIGER, a method that infers transcription factor regulomes and activities from RNA-seq data, showing improved accuracy in yeast and cancer cell lines and potential for single-cell analysis.

The paper introduces netZoo, an open-source software suite for analyzing biological networks, demonstrating its use on cancer cell line data to identify drug resistance and cancer development factors.

This paper introduces CRANE, a method to assess the robustness of network structure changes by generating ensembles of gene regulatory networks and evaluating the significance of observed community shifts.

The paper identifies NFATC4 as a key regulator of Type 2 Diabetes genes by analyzing a gene regulatory network in pancreatic islets and linking it to eQTLs and differential expression.

This paper introduces ALPACA, a method to compare genome-scale networks from different phenotypic states and identify condition-specific modules, showing its effectiveness in three biological contexts.