ArticleBriefings in bioinformatics2018
Gene co-expression analysis for functional classification and gene-disease predictions.
Article in Briefings in bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 602 papers, 1 of them a synthesis that pooled it.
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Who cites it
602 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Variable expression ofProceedings of the National Academy of Sciences of the United States of America · 2024Pooled it
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542 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
Abstract
Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes. Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease. Furthermore, we discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.
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