ArticlePLoS computational biology2017
PCSF: An R-package for network-based interpretation of high-throughput data.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed.
- Genome-wide cross-trait analysis to compare genetic architecture between Parkinson's disease and kidney-related traits.Nature communications · 2026Article
- PCIPG: A comprehensive framework for protein complex identification based on a probabilistic graphical model.PLoS computational biology · 2026Article
- XYomics: detecting sex-dependent molecular mechanisms in omics data.Nucleic acids research · 2026Article
- A data-driven framework reconstructs the molecular continuum of human MASLD progression.Nature metabolism · 2026Article
- APOE-stratified Proteomic and Metabolomic Analysis Reveals Mitochondrial Dysfunction Inflammation and Lipid Dysregulation in Alzheimer's Disease.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- OmNI: a modular open-source framework for interactive multi-omics data integration and visualization.NAR genomics and bioinformatics · 2026Article
- MmuPV1 E7 promotes phenotypes associated with "high-risk" HPV infection in mouse keratinocytes.Journal of virology · 2025Article
- Computational Approaches for Pathway-Centric Analysis of Protein Post-Translational Modifications.Proteomics · 2025Review
- Multi-omics reveal critical roles of phosphatidylcholine and sphingomyelin in antipsychotic efficacy for schizophrenia.Signal transduction and targeted therapy · 2025Article
- A protein-proximity screen reveals Ebola virus co-opts the mRNA decapping complex through the scaffold protein EDC4.Nature communications · 2025Article
- Integrative analysis of lung adenocarcinoma across diverse ethnicities and exposures.Cancer cell · 2025Article
- Multi-Omic Analysis of Glutamate Excitotoxicity in Primary Neuronal Cultures.Journal of neurochemistry · 2025Article
- Harnessing network pharmacology in drug discovery: an integrated approach.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Developmental pyrethroid exposure disrupts molecular pathways for MAP kinase and circadian rhythms in mouse brain.Physiological genomics · 2025Article
- xOmicsShiny: an R Shiny application for cross-omics data analysis and pathway mapping.Bioinformatics advances · 2025Article
- Article
- A new method for network bioinformatics identifies novel drug targets for mucinous ovarian carcinoma.NAR genomics and bioinformatics · 2024Article
- Developmental pyrethroid exposure disrupts molecular pathways for MAP kinase and circadian rhythms in mouse brain.bioRxiv : the preprint server for biology · 2024Article
- Proteome Mapping of the Human Pancreatic Islet Microenvironment Reveals Endocrine-Exocrine Signaling Sphere of Influence.Molecular & cellular proteomics : MCP · 2023Article
- Parallelized multidimensional analytic framework applied to mammary epithelial cells uncovers regulatory principles in EMT.Nature communications · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
With the recent technological developments a vast amount of high-throughput data has been profiled to understand the mechanism of complex diseases. The current bioinformatics challenge is to interpret the data and underlying biology, where efficient algorithms for analyzing heterogeneous high-throughput data using biological networks are becoming increasingly valuable. In this paper, we propose a software package based on the Prize-collecting Steiner Forest graph optimization approach. The PCSF package performs fast and user-friendly network analysis of high-throughput data by mapping the data onto a biological networks such as protein-protein interaction, gene-gene interaction or any other correlation or coexpression based networks. Using the interaction networks as a template, it determines high-confidence subnetworks relevant to the data, which potentially leads to predictions of functional units. It also interactively visualizes the resulting subnetwork with functional enrichment analysis.
Indexed as
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.