ArticlePloS one2011
Integrative network biology: graph prototyping for co-expression cancer networks.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- MetaDCN: meta-analysis framework for differential co-expression network detection with an application in breast cancer.Bioinformatics (Oxford, England) · 2017Pooled it
- Article
- Determination of a six-gene prognostic model for cervical cancer based on WGCNA combined with LASSO and Cox-PH analysis.World journal of surgical oncology · 2021Article
- Integrative enrichment analysis of gene expression based on an artificial neuron.BMC medical genomics · 2021Article
- Modular Reorganization of Signaling Networks during the Development of Colon Adenoma and Carcinoma.The journal of physical chemistry. B · 2021Article
- Identification of candidate genes in ischemic cardiomyopathy by gene expression omnibus database.BMC cardiovascular disorders · 2020Article
- Gene Coexpression Network and Module Analysis across 52 Human Tissues.BioMed research international · 2020Article
- Identifying genuine protein-protein interactions within communities of gene co-expression networks using a deconvolution method.IET systems biology · 2019Article
- Study of Meta-analysis strategies for network inference using information-theoretic approaches.BioData mining · 2017Article
- Identification of potential genes for human ischemic cardiomyopathy based on RNA-Seq data.Oncotarget · 2016Article
- Canonical correlation analysis for RNA-seq co-expression networks.Nucleic acids research · 2013Article
- Quantitative epigenetic co-variation in CpG islands and co-regulation of developmental genes.Scientific reports · 2013Article
- B-cell ligand processing pathways detected by large-scale comparative analysis.Genomics, proteomics & bioinformatics · 2012Article
- Structural measures for network biology using QuACN.BMC bioinformatics · 2011Article
- A network-based approach to classify the three domains of life.Biology direct · 2011Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Network-based analysis has been proven useful in biologically-oriented areas, e.g., to explore the dynamics and complexity of biological networks. Investigating a set of networks allows deriving general knowledge about the underlying topological and functional properties. The integrative analysis of networks typically combines networks from different studies that investigate the same or similar research questions. In order to perform an integrative analysis it is often necessary to compare the properties of matching edges across the data set. This identification of common edges is often burdensome and computational intensive. Here, we present an approach that is different from inferring a new network based on common features. Instead, we select one network as a graph prototype, which then represents a set of comparable network objects, as it has the least average distance to all other networks in the same set. We demonstrate the usefulness of the graph prototyping approach on a set of prostate cancer networks and a set of corresponding benign networks. We further show that the distances within the cancer group and the benign group are statistically different depending on the utilized distance measure.
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.