Results of sample disease state prediction using SVM algorithm with DA taxa identified by DESeq2-phyloseq as features, before and after imputation by TphPMF.

A. PR-AUC for disease state classification prediction. B. Abundance distribution of three example taxa identified by DESeq2-phyloseq after TphPMF, which were not identified before imputation, in the dataset by Yu et al. [<a href="http://www.plos...

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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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Results of sample disease state prediction using SVM algorithm with DA taxa identified by DESeq2-phyloseq as features, before and after imputation by TphPMF.

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