ArticleTheScientificWorldJournal2012
Robust microarray meta-analysis identifies differentially expressed genes for clinical prediction.
Article in TheScientificWorldJournal, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 4 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- A comprehensive meta-analysis of transcriptome data to identify signature genes associated with pancreatic ductal adenocarcinoma.PloS one · 2024Pooled it
- Pooled it
- Pooled it
- A ranking-based meta-analysis reveals let-7 family as a meta-signature for grade classification in breast cancer.PloS one · 2015Pooled it
- Meta-analysis approach as a gene selection method in class prediction: does it improve model performance? A case study in acute myeloid leukemia.BMC bioinformatics · 2017Article
- Integration of Multi-Modal Biomedical Data to Predict Cancer Grade and Patient Survival.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2016Article
- A systematic assessment of linking gene expression with genetic variants for prioritizing candidate targets.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2015Article
- Evolutionary approach for relative gene expression algorithms.TheScientificWorldJournal · 2014Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Combining multiple microarray datasets increases sample size and leads to improved reproducibility in identification of informative genes and subsequent clinical prediction. Although microarrays have increased the rate of genomic data collection, sample size is still a major issue when identifying informative genetic biomarkers. Because of this, feature selection methods often suffer from false discoveries, resulting in poorly performing predictive models. We develop a simple meta-analysis-based feature selection method that captures the knowledge in each individual dataset and combines the results using a simple rank average. In a comprehensive study that measures robustness in terms of clinical application (i.e., breast, renal, and pancreatic cancer), microarray platform heterogeneity, and classifier (i.e., logistic regression, diagonal LDA, and linear SVM), we compare the rank average meta-analysis method to five other meta-analysis methods. Results indicate that rank average meta-analysis consistently performs well compared to five other meta-analysis methods.
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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.