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

Since the beginning of the 21st century, the size, breadth, and granularityof data in biology and medicine has grown rapidly. In the example ofneuroscience, studies with thousands of subjects are becoming more common,which provide extensive phenotyping on the behavioral, neural, and genomiclevel with hundreds of variables. The complexity of such big data repositoriesoffer new opportunities and pose new challenges to investigate brain,cognition, and disease. Canonical correlation analysis (CCA) is a prototypicalfamily of methods for wrestling with and harvesting insight from such richdatasets. This doubly-multivariate tool can simultaneously consider twovariable sets from different modalities to uncover essential hiddenassociations. Our primer discusses the rationale, promises, and pitfalls of CCAin biomedicine.

Finding the needle in high-dimensional haystack: A tutorial on canonical correlation analysis