ReviewBMC medicine2023
Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges.
Review in BMC medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
What it found
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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
45 citing papers in PubMed, 1 synthesis or guideline pooled it, 77 citations in OpenAlex.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
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- Signal detection and analysis of adverse events associated with sevelamer post-marketing based on the FAERS database.Medicine · 2026Article
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- From Biomarker Discovery to Targeted Clinical Application: Addressing Translational Gaps in Early Cancer Detection.Biomedicines · 2026Article
- Machine Learning and Artificial Intelligence in Nutrition Research: Analytical Methods, Applications, and Key Considerations.The Journal of nutrition · 2026Review
- Review
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- Enhancing cancer prognostics with group penalty models: a comparative study on radiomics feature selection in lung adenocarcinomas and meningiomas.Quantitative imaging in medicine and surgery · 2026Article
- Integrative biomarker and drug target discovery in osteosarcoma: traditional experimental approaches and AI-enabled insights.Frontiers in pharmacology · 2026Review
- Resources and applications of public biomedical data.Frontiers in bioinformatics · 2026Review
- Artificial intelligence-driven therapeutics for disease modification in type 1 diabetes: a digital public health and clinical translation framework.Frontiers in public health · 2026Review
- BMI and idiopathic infertility drive polarized redox phenotypes in human follicular fluid.Frontiers in endocrinology · 2026Article
- Challenges and Opportunities for Statistics in Omics Data Analysis.Advances in experimental medicine and biology · 2026Review
- A systems microbiology framework for reproducible multi-dataset omics integration with application to long COVID.Frontiers in systems biology · 2026Article
- Early creatinine dynamics for stratifying acute kidney injury trajectories in patients with systemic lupus erythematosus: an exploratory multicenter retrospective study.Frontiers in immunology · 2026Article
- A novel statistical feature selection framework for biomarker discovery and cancer classification via multiomics integration.BMC medical research methodology · 2025Article
- Association of the dietary index for gut microbiota with metabolic syndrome and its components combining interpretable machine learning algorithms.Journal of health, population, and nutrition · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 10 institutions in 8 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many measurements across the genome, proteome, or metabolome, as well as electronic health records data that have large numbers of variables recorded for each patient. The statistical analysis of such data requires knowledge and experience, sometimes of complex methods adapted to the respective research questions.
methodsAdvances in statistical methodology and machine learning methods offer new opportunities for innovative analyses of HDD, but at the same time require a deeper understanding of some fundamental statistical concepts. Topic group TG9 "High-dimensional data" of the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative provides guidance for the analysis of observational studies, addressing particular statistical challenges and opportunities for the analysis of studies involving HDD. In this overview, we discuss key aspects of HDD analysis to provide a gentle introduction for non-statisticians and for classically trained statisticians with little experience specific to HDD.
resultsThe paper is organized with respect to subtopics that are most relevant for the analysis of HDD, in particular initial data analysis, exploratory data analysis, multiple testing, and prediction. For each subtopic, main analytical goals in HDD settings are outlined. For each of these goals, basic explanations for some commonly used analysis methods are provided. Situations are identified where traditional statistical methods cannot, or should not, be used in the HDD setting, or where adequate analytic tools are still lacking. Many key references are provided.
conclusionsThis review aims to provide a solid statistical foundation for researchers, including statisticians and non-statisticians, who are new to research with HDD or simply want to better evaluate and understand the results of HDD analyses.
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What OpenQuestion holds
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.