ArticleNPJ systems biology and applications2018
Detecting phenotype-driven transitions in regulatory network structure.
Article in NPJ systems biology and applications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed, 79 citations in OpenAlex.
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- FERRET: Framework to Evaluate Robustness in Regulatory Networks Using Heterogeneous Cell Types.bioRxiv : the preprint server for biology · 2026Article
- PARROT: Phase-Altering Regulatory Rewiring Over Time.bioRxiv : the preprint server for biology · 2026Article
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- Integrative analysis reveals therapeutic potential of pyrvinium pamoate in Merkel cell carcinoma.The Journal of clinical investigation · 2025Article
- Reproducible processing of TCGA regulatory networks.GigaScience · 2025Article
- Integrative analysis reveals therapeutic potential of pyrvinium pamoate in Merkel cell carcinoma.bioRxiv : the preprint server for biology · 2024Article
- In silico and functional analysis identifies key gene networks and novel gene candidates in obesity-linked human visceral fat.Obesity (Silver Spring, Md.) · 2024Article
- Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes.BMC pharmacology & toxicology · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes.bioRxiv : the preprint server for biology · 2023Article
- Cell-type-specific gene expression and regulation in the cerebral cortex and kidney of atypical Setbp1Journal of cellular and molecular medicine · 2023Article
- The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks.Genome biology · 2023Article
- Network-based approaches for modeling disease regulation and progression.Computational and structural biotechnology journal · 2023Review
- Network approaches for modeling the effect of drugs and diseases.Briefings in bioinformatics · 2022Review
- Identification of phenotype-specific networks from paired gene expression-cell shape imaging data.Genome research · 2022Article
- Considerations and challenges for sex-aware drug repurposing.Biology of sex differences · 2022Review
- Predicting genotype-specific gene regulatory networks.Genome research · 2022Article
- Sex differences in gene regulatory networks during mid-gestational brain development.Frontiers in human neuroscience · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Complex traits and diseases like human height or cancer are often not caused by a single mutation or genetic variant, but instead arise from functional changes in the underlying molecular network. Biological networks are known to be highly modular and contain dense "communities" of genes that carry out cellular processes, but these structures change between tissues, during development, and in disease. While many methods exist for inferring networks and analyzing their topologies separately, there is a lack of robust methods for quantifying differences in network structure. Here, we describe ALPACA (ALtered Partitions Across Community Architectures), a method for comparing two genome-scale networks derived from different phenotypic states to identify condition-specific modules. In simulations, ALPACA leads to more nuanced, sensitive, and robust module discovery than currently available network comparison methods. As an application, we use ALPACA to compare transcriptional networks in three contexts: angiogenic and non-angiogenic subtypes of ovarian cancer, human fibroblasts expressing transforming viral oncogenes, and sexual dimorphism in human breast tissue. In each case, ALPACA identifies modules enriched for processes relevant to the phenotype. For example, modules specific to angiogenic ovarian tumors are enriched for genes associated with blood vessel development, and modules found in female breast tissue are enriched for genes involved in estrogen receptor and ERK signaling. The functional relevance of these new modules suggests that not only can ALPACA identify structural changes in complex networks, but also that these changes may be relevant for characterizing biological phenotypes.
Identifiers
What OpenQuestion holds
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.