ArticleNucleic acids research2012
Discovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments.
Article in Nucleic acids research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 28 citations in OpenAlex.
- Neurodevelopment Genes Encoding Olduvai Domains Link Myalgic Encephalomyelitis to Neuropsychiatric Disorders.Diagnostics (Basel, Switzerland) · 2025Article
- Multiomics approach reveals a role of translational machinery in shaping maize kernel amino acid composition.Plant physiology · 2022Article
- A major role of TWEAK/Fn14 axis as a therapeutic target for post-angioplasty restenosis.EBioMedicine · 2019Article
- Detecting gene subnetworks under selection in biological pathways.Nucleic acids research · 2017Article
- Linearity of network proximity measures: implications for set-based queries and significance testing.Bioinformatics (Oxford, England) · 2017Article
- Web-based network analysis and visualization using CellMaps.Bioinformatics (Oxford, England) · 2016Article
- The Role of Genomic Profiling in Advanced Breast Cancer: The Two Faces of Janus.Translational oncogenomics · 2016Review
- Network analysis of gene expression in peripheral blood identifies mTOR and NF-κB pathways involved in antipsychotic-induced extrapyramidal symptoms.The pharmacogenomics journal · 2015Article
- Babelomics 5.0: functional interpretation for new generations of genomic data.Nucleic acids research · 2015Article
- Network tuned multiple rank aggregation and applications to gene ranking.BMC bioinformatics · 2015Article
- Deciphering intrafamilial phenotypic variability by exome sequencing in a Bardet-Biedl family.Molecular genetics & genomic medicine · 2014Article
- Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives.Human genetics · 2014Review
- Pathways systematically associated to Hirschsprung's disease.Orphanet journal of rare diseases · 2013Article
- Multi-dimensional prioritization of dental caries candidate genes and its enriched dense network modules.PloS one · 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
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein-protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.
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.