John F Quackenbush

BLOBFISH: Bipartite Limited Subnetworks from Multiple Observations using Breadth-First Search with Constrained Hops

BLOBFISH identifies gene regulatory subnetworks connecting predefined gene sets using breadth-first search on bipartite graphs, applied to GTEx data to explore tissue-specific relationships.

FERRET: Framework to Evaluate Robustness in Regulatory Networks Using Heterogeneous Cell Types

FERRET evaluates the robustness of single-cell gene regulatory network inference methods using biological validation and two metrics: RAUC and Monotonicity.

Genomic, Transcriptomic, and Regulomic Analyses Do Not Support Profound Autism as a Distinct Biological Category

The paper analyzes genomic, transcriptomic, and regulomic data to assess if 'profound autism' is a distinct biological category, finding no evidence for such a category.

Large-scale, interpretable gene regulatory network inference through biologically informed matrix factorization

This paper introduces Giraffe, a matrix factorization method for gene regulatory network inference that estimates signed regulatory effects, showing improved accuracy in distinguishing activating and inhibitory interactions across multiple biological contexts.

LOESS and DE-SWAN can induce artifactual "waves" of molecular aging

This paper shows that LOESS and DE-SWAN can create false 'waves' of molecular aging in data, challenging previous conclusions about discrete aging patterns.

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.

Sex differences in gene regulation and its impact on cancer incidence

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

SEAHORSE: A Serendipity Engine Assaying Heterogeneous Omics-Related Sampling Experiments

SEAHORSE systematically explores associations across heterogeneous omics data from GTEx and TCGA, revealing unexpected links between height and cancer-related pathways, and age with WNT signaling.

The Importance of Regulatory Network Structure for Complex Trait Heritability and Evolution

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

Aging-associated Alterations in the Gene Regulatory Network Landscape Associate with Risk, Prognosis and Response to Therapy in Lung Adenocarcinoma

This study identifies aging-related changes in gene regulatory networks linked to LUAD risk, prognosis, and therapy response using multi-omics data from GTEx and TCGA.

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.

Reproducible processing of TCGA regulatory networks

The paper develops a Nextflow workflow for reproducibly inferring regulatory networks from TCGA data, showing its application to colon cancer subtypes.

Selective loss of Y chromosomes in lung adenocarcinoma modulates the tumor immune environment through cancer/testis antigens

This study investigates how loss of the Y chromosome in lung adenocarcinoma affects the tumor immune environment and response to immunotherapy.

Bayesian Optimized sample-specific Networks Obtained ByOmics data (BONOBO)

BONOBO infers sample-specific co-expression networks to capture individual heterogeneity in gene regulation, outperforming existing methods in yeast, breast cancer, and thyroid tissue analyses.

Higher-order correction of persistent batch effects in correlation networks

This paper introduces COBRA, a method to correct batch effects in gene co-expression networks, showing that standard batch correction does not fully eliminate spurious co-expression associations.

Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking

This paper introduces node2vec2rank, a method for graph differential analysis that identifies biologically relevant differences between weighted networks in cancer subtypes and single-cell dynamics.

Biologically informed NeuralODEs for genome-wide regulatory dynamics

PHOENIX uses biologically informed neural ODEs to infer genome-wide regulatory dynamics, demonstrating scalability and interpretability in simulated and real data.

SpaCeNet: Spatial Cellular Networks from omics data

SpaCeNet identifies intracellular and intercellular molecular networks from spatial omics data by analyzing conditional independence relations between variables within and across cells.

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.

Predicting genotype-specific gene regulatory networks

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: Cost-effective estimation of gene regulatory networks using the Graphics Processing Unit

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

Constructing Gene Regulatory Networks using Epigenetic Data

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.

GRAND: A database of gene regulatory network models across human conditions

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

Regulation of PD1 signaling is associated with prognosis in glioblastoma multiforme

The paper identifies PD1 signaling regulation as a prognostic marker in glioblastoma using gene regulatory network analysis across two cohorts.

Using Graph Convolutional Neural Networks to Learn a Representation for Glycans

This paper introduces SweetNet, a graph convolutional neural network for glycan sequence representation, showing it outperforms existing methods and predicts organismal traits and viral glycan-binding.

Multi-omic regulatory networks capture downstream effects of kinase inhibition in Mycobacterium tuberculosis

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.

Transcriptomics data availability and reusability in the transition from microarray to next-generation sequencing

The paper analyzes transcriptomics data availability and reusability between microarray and RNA-seq experiments published between 2009 and 2013, and provides recommendations to improve RNA-seq data sharing.

Gene Regulatory Network Inference as Relaxed Graph Matching

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: PANDA Using MicroRNA Associations

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

MicroRNA-mRNA networks define translatable molecular outcome phenotypes in osteosarcoma.

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