ArticleBMC bioinformatics2012
Genes2FANs: connecting genes through functional association networks.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 34 citations in OpenAlex.
- Toxicology knowledge graph for structural birth defects.Communications medicine · 2023Article
- Article
- Comparative proteomics analysis of primary cutaneous amyloidosis.Experimental and therapeutic medicine · 2017Article
- GenePANDA-a novel network-based gene prioritizing tool for complex diseases.Scientific reports · 2017Article
- The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible.Nucleic acids research · 2017Article
- Detection of candidate biomarkers of prostate cancer progression in serum: a depletion-free 3D LC/MS quantitative proteomics pilot study.British journal of cancer · 2016Article
- The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins.Database : the journal of biological databases and curation · 2016Article
- SUR1 Receptor Interaction with Hesperidin and Linarin Predicts Possible Mechanisms of Action of Valeriana officinalis in Parkinson.Frontiers in aging neuroscience · 2016Article
- Morphine Regulated Synaptic Networks Revealed by Integrated Proteomics and Network Analysis.Molecular & cellular proteomics : MCP · 2015Article
- Publisher’s Note:Abstraction for data integration:Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction.Computational biology and chemistry · 2015Article
- Scoring the correlation of genes by their shared properties using OScal, an improved overlap quantification model.Scientific reports · 2015Article
- Lean Big Data integration in systems biology and systems pharmacology.Trends in pharmacological sciences · 2014Article
- Activating mutations in RRAS underlie a phenotype within the RASopathy spectrum and contribute to leukaemogenesis.Human molecular genetics · 2014Article
- Article
- Construction and analysis of the protein-protein interaction networks based on gene expression profiles of Parkinson's disease.PloS one · 2014Article
- Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool.BMC bioinformatics · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundProtein-protein, cell signaling, metabolic, and transcriptional interaction networks are useful for identifying connections between lists of experimentally identified genes/proteins. However, besides physical or co-expression interactions there are many ways in which pairs of genes, or their protein products, can be associated. By systematically incorporating knowledge on shared properties of genes from diverse sources to build functional association networks (FANs), researchers may be able to identify additional functional interactions between groups of genes that are not readily apparent.
resultsGenes2FANs is a web based tool and a database that utilizes 14 carefully constructed FANs and a large-scale protein-protein interaction (PPI) network to build subnetworks that connect lists of human and mouse genes. The FANs are created from mammalian gene set libraries where mouse genes are converted to their human orthologs. The tool takes as input a list of human or mouse Entrez gene symbols to produce a subnetwork and a ranked list of intermediate genes that are used to connect the query input list. In addition, users can enter any PubMed search term and then the system automatically converts the returned results to gene lists using GeneRIF. This gene list is then used as input to generate a subnetwork from the user's PubMed query. As a case study, we applied Genes2FANs to connect disease genes from 90 well-studied disorders. We find an inverse correlation between the counts of links connecting disease genes through PPI and links connecting diseases genes through FANs, separating diseases into two categories.
conclusionsGenes2FANs is a useful tool for interpreting the relationships between gene/protein lists in the context of their various functions and networks. Combining functional association interactions with physical PPIs can be useful for revealing new biology and help form hypotheses for further experimentation. Our finding that disease genes in many cancers are mostly connected through PPIs whereas other complex diseases, such as autism and type-2 diabetes, are mostly connected through FANs without PPIs, can guide better strategies for disease gene discovery. Genes2FANs is available at: http://actin.pharm.mssm.edu/genes2FANs.
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.