ArticlePloS one2009
Network analysis of differential expression for the identification of disease-causing genes.
Article in PloS one, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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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.
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Who cites it
34 citing papers in PubMed, 71 citations in OpenAlex.
- ExIR enables prioritizing driver and biomarker genes from omics data in a reference free manner.iScience · 2026Article
- PEANUT: Pathway Enrichment Analysis through Network UTilization.Bioinformatics (Oxford, England) · 2025Article
- Ant colony optimization for the identification of dysregulated gene subnetworks from expression data.BMC bioinformatics · 2024Article
- CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression.PLoS computational biology · 2024Article
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- Systems biology analysis identifiesComputational and structural biotechnology journal · 2021Article
- Genetic Factors in Antiphospholipid Syndrome: Preliminary Experience with Whole Exome Sequencing.International journal of molecular sciences · 2020Review
- Premature termination codons in theProceedings of the National Academy of Sciences of the United States of America · 2020Article
- Network hub-node prioritization of gene regulation with intra-network association.BMC bioinformatics · 2020Article
- NGSEA: Network-Based Gene Set Enrichment Analysis for Interpreting Gene Expression Phenotypes with Functional Gene Sets.Molecules and cells · 2019Article
- GeneSurrounder: network-based identification of disease genes in expression data.BMC bioinformatics · 2019Article
- HumanNet v2: human gene networks for disease research.Nucleic acids research · 2019Article
- Differential Regulatory Analysis Based on Coexpression Network in Cancer Research.BioMed research international · 2016Review
- Identification of active miRNA and transcription factor regulatory pathways in human obesity-related inflammation.BMC bioinformatics · 2015Article
- Degree-adjusted algorithm for prioritisation of candidate disease genes from gene expression and protein interactome.IET systems biology · 2014Article
- Network.assisted analysis to prioritize GWAS results: principles, methods and perspectives.Human genetics · 2014Review
- Prioritizing genes responsible for host resistance to influenza using network approaches.BMC genomics · 2013Article
- Identifying proteins controlling key disease signaling pathways.Bioinformatics (Oxford, England) · 2013Article
- An algorithm for network-based gene prioritization that encodes knowledge both in nodes and in links.PloS one · 2013Article
- Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genetic studies (in particular linkage and association studies) identify chromosomal regions involved in a disease or phenotype of interest, but those regions often contain many candidate genes, only a few of which can be followed-up for biological validation. Recently, computational methods to identify (prioritize) the most promising candidates within a region have been proposed, but they are usually not applicable to cases where little is known about the phenotype (no or few confirmed disease genes, fragmentary understanding of the biological cascades involved). We seek to overcome this limitation by replacing knowledge about the biological process by experimental data on differential gene expression between affected and healthy individuals. Considering the problem from the perspective of a gene/protein network, we assess a candidate gene by considering the level of differential expression in its neighborhood under the assumption that strong candidates will tend to be surrounded by differentially expressed neighbors. We define a notion of soft neighborhood where each gene is given a contributing weight, which decreases with the distance from the candidate gene on the protein network. To account for multiple paths between genes, we define the distance using the Laplacian exponential diffusion kernel. We score candidates by aggregating the differential expression of neighbors weighted as a function of distance. Through a randomization procedure, we rank candidates by p-values. We illustrate our approach on four monogenic diseases and successfully prioritize the known disease causing genes.
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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.