ArticlePloS one2008
Functional annotation and identification of candidate disease genes by computational analysis of normal tissue gene expression data.
Article in PloS one, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Women in the European Virus Bioinformatics Center.Viruses · 2022Article
- Survival analysis and functional annotation of long non-coding RNAs in lung adenocarcinoma.Journal of cellular and molecular medicine · 2019Article
- Inductive matrix completion for predicting gene-disease associations.Bioinformatics (Oxford, England) · 2014Article
- Drug repositioning for orphan genetic diseases through Conserved Anticoexpressed Gene Clusters (CAGCs).BMC bioinformatics · 2013Article
- An expression atlas of human primary cells: inference of gene function from coexpression networks.BMC genomics · 2013Article
- Gene prioritization of resistant rice gene against Xanthomas oryzae pv. oryzae by using text mining technologies.BioMed research international · 2013Article
- Network medicine: linking disorders.Human genetics · 2012Review
- A systems biology approach to the global analysis of transcription factors in colorectal cancer.BMC cancer · 2012Article
- Functional genome annotation of Drosophila seminal fluid proteins using transcriptional genetic networks.Genetics research · 2011Article
- A network of genes, genetic disorders, and brain areas.PloS one · 2011Article
- Candidate gene prioritization based on spatially mapped gene expression: an application to XLMR.Bioinformatics (Oxford, England) · 2010Article
- ORTom: a multi-species approach based on conserved co-expression to identify putative functional relationships among genes in tomato.Plant molecular biology · 2010Article
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Authors and funding
7 authors.
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Abstract
backgroundHigh-throughput gene expression data can predict gene function through the "guilt by association" principle: coexpressed genes are likely to be functionally associated. METHODOLOGY/PRINCIPAL
findingsWe analyzed publicly available expression data on normal human tissues. The analysis is based on the integration of data obtained with two experimental platforms (microarrays and SAGE) and of various measures of dissimilarity between expression profiles. The building blocks of the procedure are the Ranked Coexpression Groups (RCG), small sets of tightly coexpressed genes which are analyzed in terms of functional annotation. Functionally characterized RCGs are selected by means of the majority rule and used to predict new functional annotations. Functionally characterized RCGs are enriched in groups of genes associated to similar phenotypes. We exploit this fact to find new candidate disease genes for many OMIM phenotypes of unknown molecular origin. CONCLUSIONS/SIGNIFICANCE: We predict new functional annotations for many human genes, showing that the integration of different data sets and coexpression measures significantly improves the scope of the results. Combining gene expression data, functional annotation and known phenotype-gene associations we provide candidate genes for several genetic diseases of unknown molecular basis.
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