ArticleScientific reports2019
Integrating gene regulatory pathways into differential network analysis of gene expression data.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comprehensive functional network analysis and screening of deleterious pathogenic variants in non-syndromic hearing loss causative genes.Bioscience reports · 2021Pooled it
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- Double optimal transport for differential gene regulatory network inference with unpaired samples.Bioinformatics (Oxford, England) · 2025Article
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- Transcriptomic Evaluation of a Stress Vulnerability Network Using Single-Cell RNA Sequencing in Mouse Prefrontal Cortex.Biological psychiatry · 2024Article
- Non parametric differential network analysis: a tool for unveiling specific molecular signatures.BMC bioinformatics · 2024Article
- Structural analysis of genomic and proteomic signatures reveal dynamic expression of intrinsically disordered regions in breast cancer.iScience · 2024Article
- Single cell transcriptomes and multiscale networks from persons with and without Alzheimer's disease.Nature communications · 2024Article
- Biomarkers from subcutaneous engineered tissues predict acute rejection of organ allografts.Science advances · 2024Article
- Differential Gene Regulatory Network Analysis between Azacitidine-Sensitive and -Resistant Cell Lines.International journal of molecular sciences · 2024Article
- PRANA: an R package for differential co-expression network analysis with the presence of additional covariates.BMC genomics · 2023Article
- Semantic Representation of Neural Circuit Knowledge inbioRxiv : the preprint server for biology · 2023Article
- MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms.Nature communications · 2023Article
- Comorbidity genetic risk and pathways impact SARS-CoV-2 infection outcomes.Scientific reports · 2023Article
- Analysis of exome data in a UK cohort of 603 patients with syndromic orofacial clefting identifies causal molecular pathways.Human molecular genetics · 2023Review
- A pseudo-value regression approach for differential network analysis of co-expression data.BMC bioinformatics · 2023Article
- Unraveling Plant-Pathogen Interactions in Cereals Using RNA-seq.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Identification of shared biological features in four different lung cell lines infected with SARS-CoV-2 virus through RNA-seq analysis.Frontiers in genetics · 2023Article
- Multi-tissue RNA-Seq Analysis and Long-read-based Genome Assembly Reveal Complex Sex-specific Gene Regulation and Molecular Evolution in the Manila Clam.Genome biology and evolution · 2022Article
- Network autonomic analysis of post-acute sequelae of COVID-19 and postural tachycardia syndrome.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2022Article
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3 authors.
Funding
Abstract
The advent of next-generation sequencing has introduced new opportunities in analyzing gene expression data. Research in systems biology has taken advantage of these opportunities by gleaning insights into gene regulatory networks through the analysis of gene association networks. Contrasting networks from different populations can reveal the many different roles genes fill, which can lead to new discoveries in gene function. Pathologies can also arise from aberrations in these gene-gene interactions. Exposing these network irregularities provides a new avenue for understanding and treating diseases. A general framework for integrating known gene regulatory pathways into a differential network analysis between two populations is proposed. The framework importantly allows for any gene-gene association measure to be used, and inference is carried out through permutation testing. A simulation study investigates the performance in identifying differentially connected genes when incorporating known pathways, even if the pathway knowledge is partially inaccurate. Another simulation study compares the general framework with four state-of-the-art methods. Two RNA-seq datasets are analyzed to illustrate the use of this framework in practice. In both examples, the analysis reveals genes and pathways that are known to be biologically significant along with potentially novel findings that may be used to motivate future research.
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