ArticleBMC systems biology2016
High-dimensional omics data analysis using a variable screening protocol with prior knowledge integration (SKI).
Article in BMC systems biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Rank-based learning: a novel high-throughput algorithm resilient to missing data and effective for datasets with small sample size.Briefings in bioinformatics · 2025Article
- Statistical model building: Background "knowledge" based on inappropriate preselection causes misspecification.BMC medical research methodology · 2021Article
- Fabrication approaches for high-throughput and biomimetic disease modeling.Acta biomaterialia · 2021Review
- Coupling sparse Cox models with clustering of longitudinal transcriptomics data for trauma prognosis.BioData mining · 2021Article
- Article
- Ensembles of natural language processing systems for portable phenotyping solutions.Journal of biomedical informatics · 2019Article
- A Novel Joint Gene Set Analysis Framework Improves Identification of Enriched Pathways in Cross Disease Transcriptomic Analysis.Frontiers in genetics · 2019Article
- Application of Sparse Linear Discriminant Analysis and Elastic Net for Diagnosis of IgA Nephropathy: Statistical and Biological ViewpointsIranian biomedical journal · 2018Article
- Integrated analysis of DNA-methylation and gene expression using high-dimensional penalized regression: a cohort study on bone mineral density in postmenopausal women.BMC medical genomics · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHigh-throughput technology could generate thousands to millions biomarker measurements in one experiment. However, results from high throughput analysis are often barely reproducible due to small sample size. Different statistical methods have been proposed to tackle this "small n and large p" scenario, for example different datasets could be pooled or integrated together to provide an effective way to improve reproducibility. However, the raw data is either unavailable or hard to integrate due to different experimental conditions, thus there is an emerging need to develop a method for "knowledge integration" in high-throughput data analysis.
resultsIn this study, we proposed an integrative prescreening approach, SKI, for high-throughput data analysis. A new rank is generated based on two initial ranks: (1) knowledge based rank; and (2) marginal correlation based rank. Our simulation shows the SKI outperforms other methods without knowledge-integration in terms of higher true positive rate given the same number of variables selected. We also applied our method in a drug response study and found its performance to be better than regular screening methods.
conclusionThe proposed method provides an effective way to integrate knowledge for high-throughput analysis. It could easily implemented with our provided R package named SKI.
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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.