Megha Padi

PARROT: Phase-Altering Regulatory Rewiring Over Time

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

Probabilistic network modeling identifies RBPJ as a driver of stemness in Merkel cell carcinoma

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.

Transient centrosome loss in cultured prostate epithelial cells induces chromosomal instability to produce an oncogenic genotype that correlates with poor clinical outcomes

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

Viral and host network analysis of the human cytomegalovirus transcriptome in latency.

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.

Identifying Sex Differences in Lung Adenocarcinoma Using Multi-Omics Integrative Protein Signaling Networks

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

Joint inference of transcription factor activity and context-specific regulatory networks

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 Network Zoo: a multilingual package for the inference and analysis of biological networks

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.

Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules

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.

Controllability in an islet specific regulatory network identifies the transcriptional factor NFATC4, which regulates Type 2 Diabetes associated genes

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.

Phenotype-Driven Transitions In Regulatory Network Structure

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.