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Machine learning for screening hydronephrosis signature genes Based on the GEO dataset GSE96101, the R package glmnet was utilized to perform the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression analysis
However, research and practical fitness experience point towards a general dosage range that works well for most people
Marso SP, Bain SC, Consoli A, Eliaschewitz FG, Jdar E, Leiter LA, Lingvay I, Rosenstock J, Seufert J, Warren ML
We approached this by identifying omics- features (i.e., expression, mutation, copy number, pathways, metabolites, etc.) that are predictive of NRF2 CRISPR knockout scores from the DepMap
Changes are subtle in the early days
It operates differently than previous choices
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