Kimberly Glass

The paper identifies protein interactors of COPD GWAS genes using AP-MS and network analysis, revealing a disease network module involving 8 proteins.

This study investigates CREB5's role in skin wound healing by comparing its effects in oral and skin keratinocytes, showing improved migration with CREB5 overexpression.

The paper identifies smoking-associated miRNA regulatory networks in blood and tissue, revealing NDUFA12 as a key target linked to mitochondrial dysfunction in smokers.

The paper investigates HHIP's role in epithelial wound healing and links its dysfunction to COPD susceptibility through computational modeling and experimental validation.

The paper identifies sex-biased regulation of cancer genes in normal tissues, linking these differences to sex-specific cancer incidence patterns.

This paper analyzes single-sample network inference methods, identifying trade-offs between accuracy and specificity and suggesting opportunities for methodological synergy.

The paper identifies HHIP protein interactions in lung cells, revealing new connections to COPD GWAS genes and providing insights into COPD pathogenesis.

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 Boolean model to simulate mitochondrial dynamics and senescence processes, identifying mechanisms of MiDAS and its relationship to cancer and aging.

This study investigates the metabolic role of vitamin D in children's neurodevelopment using a network approach, finding associations between vitamin D deficiency and changes in tryptophan, linoleic, and fatty acid metabolism linked to ASD-related phenotypes.

The paper improves transcription factor binding prediction by integrating CpG methylation data with PWM-based scores, showing better performance than standard PWM scores for most TFs.

The paper introduces SNAIL, a normalization method that reduces false-positive co-expression associations in RNA-sequencing data, improving downstream network analysis.

The paper introduces MuXTalk, a network-based method to detect signaling crosstalk by integrating signaling and regulatory interactions, expanding crosstalk databases and improving prediction performance.

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.

gpuZoo accelerates gene regulatory network inference by using GPU acceleration, achieving faster and cheaper runtime compared to CPU-based methods.

This paper introduces SPIDER, a method that integrates DNase-Seq data into a message passing framework to construct gene regulatory networks, showing improved accuracy over existing methods.

The paper describes applying LIONESS to reconstruct sample-specific networks using different algorithms and data types, discussing preprocessing impacts and analysis methods.

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

This study identifies the genomic signatures of the unjamming transition in human bronchial epithelial cells using mechanical compression and RNA-Seq.

The paper analyzes the robustness of multilayer biological networks by integrating gene regulatory, protein-protein interaction, and metabolic data to assess how perturbations affect system stability.

The paper identifies combinatorial functions of histone demethylases in gene regulation of mouse embryonic stem cells by analyzing their genome-wide binding patterns.

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.

PUMA integrates miRNA target predictions with gene expression data to identify tissue-specific miRNA regulatory interactions and their disease relevance.

The paper uses SWIM software to analyze COPD transcriptomic networks, identifying key modules and switch genes associated with immune response, inflammation, and hypoxia.

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 identifies peripheral and core genes in the human interactome using gene expression data and shows their distinct biological roles and implications for disease classification and drug targets.

This paper introduces six new statistical tests for differential correlation networks that are computationally efficient and outperform existing methods in simulations and real data analysis.

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.