Fig. 4: QLattice classification analysis of TA multi-omics data and FLT/GC groups. | npj Microgravity

Fig. 4: QLattice classification analysis of TA multi-omics data and FLT/GC groups.

From: Explainable machine learning identifies multi-omics signatures of muscle response to spaceflight in mice

Fig. 4

a Representative examples of the mathematical relationships between multi-omic features identified by QLattice to predict FLT versus GC in TA muscle during LOOCV. b Top 11 features ranked by how many times they were used in a model found by QLattice during LOOCV. c T1 and T10 cross-validated R2 scores, as well as the total number of RNA-seq, proteomics, and methylation features across all models. d Gene set enrichment analysis results using the top 11 features from the QLattice analysis.

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