ArticleF1000Research2016
Creating, generating and comparing random network models with NetworkRandomizer.
Article in F1000Research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- A journey through the molecular networks of endomyocardial biopsies to explore ATTR and ALiScience · 2026Article
- Dipeptidyl peptidase 3 sets the threshold for immune activation and survival during experimental bacterial infection.Nature communications · 2026Article
- Host Species Mediate Distinct Seed Microbiome Responses to Restoration.Microbial ecology · 2026Article
- Effects of CP4-EPSPS-Containing Brassica juncea Hybrids on the Gut and Fecal Microbiota of the Terrestrial Decomposer Armadillidium vulgare.Microbial ecology · 2025Article
- CoPPIs algorithm: a tool to unravel protein cooperative strategies in pathophysiological conditions.Briefings in bioinformatics · 2025Article
- Article
- Integrated Network-Based Analysis of Diseases Associated with Amyloid Deposition Through a Disease-Protein-Drug Network.Pharmaceuticals (Basel, Switzerland) · 2024Article
- The protein interactome of the citrus Huanglongbing pathogen Candidatus Liberibacter asiaticus.Nature communications · 2023Article
- Article
- The Protein Network in Subcutaneous Fat Biopsies from Patients with AL Amyloidosis: More Than Diagnosis?Cells · 2023Article
- Trojan-horse silk fibroin nanocarriers loaded with a re-call antigen to redirect immunity against cancer.Journal for immunotherapy of cancer · 2023Article
- Rhizosphere analysis of field-grown Panax ginseng with different degrees of red skin provides the basis for preventing red skin syndrome.BMC microbiology · 2022Article
- Organic Connection of Holobiont Components and the Essential Roles of Core Microbes in the Holobiont Formation of FeralFrontiers in microbiology · 2022Article
- Protein Integrated Network Analysis to Reveal Potential Drug Targets Against Extended Drug-Resistant Mycobacterium tuberculosis XDR1219.Molecular biotechnology · 2021Article
- Centrality of drug targets in protein networks.BMC bioinformatics · 2021Article
- Network-based analysis of key regulatory genes implicated in Type 2 Diabetes Mellitus and Recurrent Miscarriages in Turner Syndrome.Scientific reports · 2021Article
- Topological clustering of regulatory genes confers pathogenic tolerance to cassava brown streak virus (CBSV) in cassava.Scientific reports · 2021Article
- Genome-wide inference of the Camponotus floridanus protein-protein interaction network using homologous mapping and interacting domain profile pairs.Scientific reports · 2020Article
- Deciphering the Novel Target Genes Involved in the Epigenetics of Hepatocellular Carcinoma Using Graph Theory Approach.Current genomics · 2019Article
- WIPER: Weighted in-Path Edge Ranking for biomolecular association networks.Quantitative biology (Beijing, China) · 2019Article
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
No grant is acknowledged in the PubMed record.
Abstract
Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. To fill the validation gap we present an app, for the Cytoscape platform, which aims at creating randomised networks and randomising existing, real networks. Since there is a lack of tools that allow performing such operations, our app aims at enabling researchers to exploit different, well known random network models that could be used as a benchmark for validating real, biological datasets. We also propose a novel methodology for creating random weighted networks, i.e. the multiplication algorithm, starting from real, quantitative data. Finally, the app provides a statistical tool that compares real versus randomly computed attributes, in order to validate the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.
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.