ArticleiScience2019
Estimating Sample-Specific Regulatory Networks.
Article in iScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 121 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
121 citing papers in PubMed.
- Leiomodin 1 deficiency promotes lipid accumulation and redirects gene regulatory programs in smooth muscle cells exposed to oxidized LDL.Research square · 2026Article
- Ten quick tips for causal analysis of biomedical omics data.PLoS computational biology · 2026Article
- Network-based discovery of regulatory drivers of cognitive decline in alzheimer's disease.npj aging · 2026Article
- Temporal changes in gene regulation during human tissue repair.Scientific reports · 2026Article
- Care demand networks in maternity care - an innovative approach exploring the complexity of care demands with routine data: Retrospective observational study.International journal of nursing studies advances · 2026Article
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- ssNetShift: single-sample metabolic network rewiring reveals hidden prognostic subtypes beyond clinical staging in gastric cancer.Briefings in bioinformatics · 2026Article
- MicroSSNet: an R package for microbial network construction and analysis at the single-sample and aggregated levels.BMC bioinformatics · 2026Article
- Integrated spatial transcriptomics and pan-cancer XGBoost modeling uncover spatial drivers of immune exclusion and predict immunotherapy response.Cancer immunology, immunotherapy : CII · 2026Article
- Pseudogene Coexpression Networks Reveal a Robust Prognostic Signature for Pediatric B-ALL Survival.Cancer research communications · 2026Article
- Personalized Morphology, Replication Timing, and RNA based Gene Expression Networks for Basal-like and Classical subtyping genes in Pancreatic Adenocarcinoma.bioRxiv : the preprint server for biology · 2026Article
- Challenges and Opportunities in Single-Sample Network Modeling.bioRxiv : the preprint server for biology · 2026Article
- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
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- E-ABIN: an explainable module for anomaly detection in biological networks.NAR genomics and bioinformatics · 2025Article
- Sex differences in gene regulation and its impact on cancer incidence.bioRxiv : the preprint server for biology · 2025Article
- ScReNI: Single-cell Regulatory Network Inference Through Integrating scRNA-seq and scATAC-seq Data.Genomics, proteomics & bioinformatics · 2025Article
- Dynamic Network Driver Analysis Identifies Master Factors Associated with Progression of Solar Lentigines.Biology · 2025Article
- Aging-associated alterations in gene regulatory networks associate with risk, prognosis and response to therapy in lung adenocarcinoma.npj aging · 2025Article
61 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Biological systems are driven by intricate interactions among molecules. Many methods have been developed that draw on large numbers of expression samples to infer connections between genes (or their products). The result is an aggregate network representing a single estimate for the likelihood of each interaction, or "edge," in the network. Although informative, aggregate models fail to capture population heterogeneity. Here we propose a method to reverse engineer sample-specific networks from aggregate networks. We demonstrate our approach in several contexts, including simulated, yeast microarray, and human lymphoblastoid cell line RNA sequencing data. We use these sample-specific networks to study changes in network topology across time and to characterize shifts in gene regulation that were not apparent in the expression data. We believe that generating sample-specific networks will greatly facilitate the application of network methods to large, complex, and heterogeneous multi-omic datasets, supporting the emerging field of precision network medicine.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.