ArticleEntropy (Basel, Switzerland)2021
Aggregating Knockoffs for False Discovery Rate Control with an Application to Gut Microbiome Data.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed, 5 citations in OpenAlex.
- Higher BMI and extraversion are associated with greater button-press force in a lab setting.Frontiers in public health · 2025Article
- mbtransfer: Microbiome intervention analysis using transfer functions and mirror statistics.PLoS computational biology · 2024Article
- Comprehensive data optimization and risk prediction framework: machine learning methods for inflammatory bowel disease prediction based on the human gut microbiome data.Frontiers in microbiology · 2024Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
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Abstract
Recent discoveries suggest that our gut microbiome plays an important role in our health and wellbeing. However, the gut microbiome data are intricate; for example, the microbial diversity in the gut makes the data high-dimensional. While there are dedicated high-dimensional methods, such as the lasso estimator, they always come with the risk of false discoveries. Knockoffs are a recent approach to control the number of false discoveries. In this paper, we show that knockoffs can be aggregated to increase power while retaining sharp control over the false discoveries. We support our method both in theory and simulations, and we show that it can lead to new discoveries on microbiome data from the American Gut Project. In particular, our results indicate that several phyla that have been overlooked so far are associated with obesity.
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Registered trials
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