ArticleNucleic acids research2016
WeGET: predicting new genes for molecular systems by weighted co-expression.
Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Translation efficiency covariation identifies conserved coordination patterns across cell types.Nature biotechnology · 2026Article
- COXPRESdb v8: an animal gene coexpression database navigating from a global view to detailed investigations.Nucleic acids research · 2023Article
- A cross-species co-functional gene network underlying leaf senescence.Horticulture research · 2023Article
- Integrated network analyses identify MYB4R1 neofunctionalization in the UV-B adaptation of Tartary buckwheat.Plant communications · 2022Article
- Peripheral Ion Channel Gene Screening in Painful- and Painless-Diabetic Neuropathy.International journal of molecular sciences · 2022Article
- Machine learning prediction of novel pectinolytic enzymes inMicrobial genomics · 2021Article
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- Mutations in PDLIM5 are rare in dilated cardiomyopathy but are emerging as potential disease modifiers.Molecular genetics & genomic medicine · 2020Article
- Identifying gene function and module connections by the integration of multispecies expression compendia.Genome research · 2019Article
- Mining conditions specific hub genes from RNA-Seq gene-expression data via biclustering and their application to drug discovery.IET systems biology · 2019Article
- COSCEB: Comprehensive search for column-coherent evolution biclusters and its application to hub gene identification.Journal of biosciences · 2019Article
- Latent network-based representations for large-scale gene expression data analysis.BMC bioinformatics · 2019Article
- A Combined Mass Spectrometry and Data Integration Approach to Predict the Mitochondrial Poly(A) RNA Interacting Proteome.Frontiers in cell and developmental biology · 2019Article
- Article
- CLIC, a tool for expanding biological pathways based on co-expression across thousands of datasets.PLoS computational biology · 2017Article
- Genome-wide association studies identified multiple genetic loci for body size at four growth stages in Chinese Holstein cattle.PloS one · 2017Article
- Exploiting single-cell expression to characterize co-expression replicability.Genome biology · 2016Article
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Authors and funding
7 authors.
Funding
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Abstract
We have developed the Weighted Gene Expression Tool and database (WeGET, http://weget.cmbi.umcn.nl) for the prediction of new genes of a molecular system by correlated gene expression. WeGET utilizes a compendium of 465 human and 560 murine gene expression datasets that have been collected from multiple tissues under a wide range of experimental conditions. It exploits this abundance of expression data by assigning a high weight to datasets in which the known genes of a molecular system are harmoniously up- and down-regulated. WeGET ranks new candidate genes by calculating their weighted co-expression with that system. A weighted rank is calculated for human genes and their mouse orthologs. Then, an integrated gene rank and p-value is computed using a rank-order statistic. We applied our method to predict novel genes that have a high degree of co-expression with Gene Ontology terms and pathways from KEGG and Reactome. For each query set we provide a list of predicted novel genes, computed weights for transcription datasets used and cell and tissue types that contributed to the final predictions. The performance for each query set is assessed by 10-fold cross-validation. Finally, users can use the WeGET to predict novel genes that co-express with a custom query set.
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