John Platig

The paper analyzes SNP heritability across eQTL networks to show heritability clusters in tissue-specific modules and local hubs, which influence complex trait evolution.

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.

The paper introduces EGRET, a method to infer genotype-specific gene regulatory networks and identifies regulatory differences associated with diseases in specific cell types.

GRAND is a database of gene regulatory network models across human conditions, enabling comparison between biological states and drug response prediction.

The paper shows that kinase inhibition in Mtb affects regulatory networks, altering cell wall integrity, stress response, and energy production through transcription factor activity changes.

This paper presents a theoretical framework for PANDA, a gene regulatory network inference method, and shows it outperforms state-of-the-art methods in predicting transcription factor binding.

The paper assesses the reproducibility and functional importance of eQTL network nodes by analyzing degree centrality across 21 GTEx tissues.

The paper identifies genomic variants associated with immune cell infiltration using histopathological image QTL analysis.

The paper develops a preprocessing and normalization pipeline for large-scale RNA-Seq data, addressing batch and tissue effects in heterogeneous datasets.

This study analyzes gene regulation across 38 tissues to show that transcription factor-target connections are more tissue-specific than genes, and that tissue-specific genes are not highly targeted in their networks but occupy bottleneck positions.

The paper compares gene expression and regulatory patterns between cell lines and their source tissues, finding reduced expression levels and altered transcription factor regulation in cell lines.

The paper uses a network-based approach to analyze eQTLs across 13 tissues, identifying cis- and trans-eQTLs and linking SNPs to biological processes and disease associations.

The paper identifies sexual dimorphism in gene expression and regulatory networks across 31 human tissues using GTEx data, showing that many genes and networks are sex-specific.