ArticleBMC medical genomics2017
Differential gene expression in disease: a comparison between high-throughput studies and the literature.
Article in BMC medical genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 3 of them syntheses that pooled it.
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Who cites it
53 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A meta-analysis of bulk RNA-seq datasets identifies potential biomarkers and repurposable therapeutics against Alzheimer's disease.Scientific reports · 2024Pooled it
- Identifying Novel Osteoarthritis-Associated Genes in Human Cartilage Using a Systematic Meta-Analysis and a Multi-Source Integrated Network.International journal of molecular sciences · 2022Pooled it
- What genes are differentially expressed in individuals with schizophrenia? A systematic review.Molecular psychiatry · 2022Pooled it
- Decoding the Transcriptional Complexity of the Human BRCA2 DNA Repair Gene Using Hybrid-seq.Biochemical genetics · 2026Article
- Analyzing the Molecular Signature Genes and Pathways of Dengue Fever, Dengue Hemorrhagic Fever and Dengue Shock Syndrome Caused by Dengue Virus in India.Molecular biotechnology · 2026Article
- Transcriptomic insights into the healthspan-enhancing effects of C. chinensis seed and E. ulmoides bark extracts in Caenorhabditis elegans.Biogerontology · 2025Article
- A transcriptional plasticity-aware framework for RNA-seq differential expression analysis.Briefings in bioinformatics · 2025Article
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- Efficient and easy gene expression and genetic variation data analysis and visualization using exvar.Scientific reports · 2025Article
- Technical and legal aspects of federated learning in bioinformatics: applications, challenges and opportunities.Frontiers in digital health · 2025Review
- A statistical approach for systematic identification of transition cells from scRNA-seq data.Cell reports methods · 2024Article
- Identification of novel risk genes for Alzheimer's disease by integrating genetics from hippocampus.Scientific reports · 2024Article
- Gene signatures for cancer research: A 25-year retrospective and future avenues.PLoS computational biology · 2024Article
- Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling.Nature biotechnology · 2024Article
- Integration of Image Pattern Recognition and Photon Sensor for Analyzing Cytokine Gene Expression Using πCode MicroDisc.Biosensors · 2024Article
- Ethnicity-Based Variations in Focal Adhesion Kinase Signaling in Glioblastoma Gene Expression: A Study of the Puerto Rican Hispanic Population.International journal of molecular sciences · 2024Article
- Implications of ZNF334 gene in lymph node metastasis of lung SCC: potential bypassing of cellular senescence.Journal of translational medicine · 2024Article
- Senescence evolution under the catastrophic accumulation of deleterious mutations.Evolution letters · 2024Article
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- Meta-Research: understudied genes are lost in a leaky pipeline between genome-wide assays and reporting of results.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDifferential gene expression is important to understand the biological differences between healthy and diseased states. Two common sources of differential gene expression data are microarray studies and the biomedical literature.
methodsWith the aid of text mining and gene expression analysis we have examined the comparative properties of these two sources of differential gene expression data.
resultsThe literature shows a preference for reporting genes associated to higher fold changes in microarray data, rather than genes that are simply significantly differentially expressed. Thus, the resemblance between the literature and microarray data increases when the fold-change threshold for microarray data is increased. Moreover, the literature has a reporting preference for differentially expressed genes that (1) are overexpressed rather than underexpressed; (2) are overexpressed in multiple diseases; and (3) are popular in the biomedical literature at large. Additionally, the degree to which diseases are similar depends on whether microarray data or the literature is used to compare them. Finally, vaguely-qualified reports of differential expression magnitudes in the literature have only small correlation with microarray fold-change data.
conclusionsReporting biases of differential gene expression in the literature can be affecting our appreciation of disease biology and of the degree of similarity that actually exists between different diseases.
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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.