Comparison of the performance of TphPMF with other imputation methods in Simulation 1 and Simulation 2.

Comparison of the performance of TphPMF with other imputation methods in Simulation 1 and Simulation 2.

<p><b>A.</b> Mean squared error (MSE) between imputed and complete data (Performed 100 iterations). <b>B.</b> The mean Pearson correlation between imputed and complete data across all taxa (Performed 100 iterations). <b>C.</b> Distribution of taxa abundance...

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Bibliographic Details
Main Author:	Xinyu Han (2237545) (author)
Other Authors:	Kai Song (107003) (author)
Published:	2025
Subjects:	Biochemistry Microbiology Cell Biology Genetics Ecology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified type 2 diabetes predicting disease conditions particularly showing advantages machine learning approach detecting differentially abundant microbiome datasets pertaining data sparsity represents microbiome data imputation tphpmf significantly improves sparse data microbiome research xlink "> tphpmf enhances thereby undermining termed tphpmf statistical analyses potential non formidable challenge biological zeros
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