John F Quackenbush

The paper identifies microRNA-mRNA networks associated with prognosis in osteosarcoma and validates their predictive value across independent datasets.

This paper shows that molecular subtypes and gene signatures in ovarian cancer are influenced by stromal cell content, suggesting that bulk tissue analysis may be unreliable without accounting for cell admixture.

lionessR provides an R implementation of LIONESS for single-sample network reconstruction using any network method that outputs a weighted adjacency matrix, demonstrating its use on a bone cancer dataset.

The paper evaluates adjusted spectral clustering methods for detecting gene clusters in eQTL data, considering regression errors in network construction.

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

The paper identifies sex-linked differences in colon cancer drug metabolism via gene regulatory network analysis, showing improved survival in females with higher pathway targeting.

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

This study identifies cancer subtypes using somatic mutation data from The Cancer Genome Atlas, finding associations with prognostic outcomes and treatment options.

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.

This paper introduces MONSTER, a method to identify transcription factor drivers of cell state transitions using gene regulatory network models, applied to COPD studies to find persistent regulatory signals.

WebMeV provides a cloud-based platform for analyzing and visualizing cancer genomic data, enabling users to access and explore large datasets with advanced analysis tools.

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.

This paper introduces a normalization method for genomic data that accounts for biological differences in distributions while reducing technical variation.

This paper discusses challenges and future directions in single-cell analysis, highlighting advances in technologies and computational methods for studying cellular heterogeneity.

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.