English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

A metabolite may be expressed on a group of variables in mass-spectrometry experiments. Evaluation on metabolite effects should consider this group. Group lasso regression can be used to evaluate these groups. It shrinks some regression coefficients to zero by intermediate penalty on OLS loss function. The data used were antioxidant activity and mass/charge ion from LC-MS output of Curcuma aeruginosa compositions of 3 areas in Java. The significance metabolite groups were 148,060, 202,179, 204,159, 228,123, 238,150, 246,133, 312,274, and 398,335. Keywords— Variable-group selection, group lasso regression, antioxidant, Curcuma aeruginosa

Variable-Group Selection on Estimated Metabolites of Curcuma aeruginosa Related to Antioxidant Activity by Using Group Lasso Regression