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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Optimal biomarker combinations for treatment-selection can be derived byminimizing total burden to the population caused by the targeted disease andits treatment. However, when multiple biomarkers are present, including all inthe model can be expensive and hurt model performance. To remedy this, weconsider feature selection in optimization by minimizing an extended totalburden that additionally incorporates biomarker measurement costs. Formulatingit as a 0-norm penalized weighted classification, we develop various proceduresfor estimating linear and nonlinear combinations. Through simulations and areal data example, we demonstrate the importance of incorporatingfeature-selection and marker cost when deriving treatment-selection rules.

Selecting Biomarkers for building optimal treatment selection rules using Kernel Machines