Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats.

Front Endocrinol (Lausanne) · 2024 · PMC11157016 · PMID 38854687

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highly significantno p-value reported

Specifically, we selected two datasets, GSE20966 and GSE142025 , and used the “GEO2R/Limma R” package to screen for highly significant differentially expressed genes (DEGs).

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