ArticleBioinformatics (Oxford, England)2014
PGS: a tool for association study of high-dimensional microRNA expression data with repeated measures.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
6 citing papers in PubMed.
- MicroRNA Databases and Tools.Methods in molecular biology (Clifton, N.J.) · 2022Article
- GEE-TGDR: A Longitudinal Feature Selection Algorithm and Its Application to lncRNA Expression Profiles for Psoriasis Patients Treated with Immune Therapies.BioMed research international · 2021Article
- Feature Selection for Longitudinal Data by Using Sign Averages to Summarize Gene Expression Values over Time.BioMed research international · 2019Article
- Incorporating Pathway Information into Feature Selection towards Better Performed Gene Signatures.BioMed research international · 2019Review
- Ultra-high dimensional variable selection with application to normative aging study: DNA methylation and metabolic syndrome.BMC bioinformatics · 2017Article
- High-dimensional omics data analysis using a variable screening protocol with prior knowledge integration (SKI).BMC systems biology · 2016Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
motivationMicroRNAs (miRNAs) are short single-stranded non-coding molecules that usually function as negative regulators to silence or suppress gene expression. Owning to the dynamic nature of miRNA and reduced microarray and sequencing costs, a growing number of researchers are now measuring high-dimensional miRNA expression data using repeated or multiple measures in which each individual has more than one sample collected and measured over time. However, the commonly used univariate association testing or the site-by-site (SBS) testing may underutilize the longitudinal feature of the data, leading to underpowered results and less biologically meaningful results.
resultsWe propose a penalized regression model incorporating grid search method (PGS), for analyzing associations of high-dimensional miRNA expression data with repeated measures. The development of this analytical framework was motivated by a real-world miRNA dataset. Comparisons between PGS and the SBS testing revealed that PGS provided smaller phenotype prediction errors and higher enrichment of phenotype-related biological pathways than the SBS testing. Our extensive simulations showed that PGS provided more accurate estimates and higher sensitivity than the SBS testing with comparable specificities. AVAILABILITY AND IMPLEMENTATION: R source code for PGS algorithm, implementation example and simulation study are available for download at https://github.com/feizhe/PGS.
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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.