ArticleFrontiers in genetics2020
Enhanced Permutation Tests via Multiple Pruning.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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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Who cites it
7 citing papers in PubMed.
- Precision connectivity in osteoarthritis pain with permutation and network analysis: a key step toward clinical application.BMC medical imaging · 2025Article
- Precision Connectivity in Arthritis Pain with Permutation and Network Analysis: A Key Step Toward Clinical Application.Research square · 2025Article
- Enhanced adaptive permutation test with negative binomial distribution in genome-wide omics datasets.Genes & genomics · 2025Article
- Addressing brain metabolic connectivity in treatment-resistant schizophrenia: a novel graph theory-driven application ofSchizophrenia (Heidelberg, Germany) · 2024Article
- Article
- Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study.International journal of clinical practice · 2023Article
- Immunoprofiling of Nonarteritic Anterior Ischemic Optic Neuropathy.Translational vision science & technology · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Big multi-omics data in bioinformatics often consists of a huge number of features and relatively small numbers of samples. In addition, features from multi-omics data have their own specific characteristics depending on whether they are from genomics, proteomics, metabolomics, etc. Due to these distinct characteristics, standard statistical analyses using parametric-based assumptions may sometimes fail to provide exact asymptotic results. To resolve this issue, permutation tests can be a way to exactly analyze multi-omics data because they are distribution-free and flexible to use. In permutation tests,
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