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

We propose a novel hierarchical model for multitask bipartite ranking. Theproposed approach combines a matrix-variate Gaussian process with a generativemodel for task-wise bipartite ranking. In addition, we employ a novel traceconstrained variational inference approach to impose low rank structure on theposterior matrix-variate Gaussian process. The resulting posterior covariancefunction is derived in closed form, and the posterior mean function is thesolution to a matrix-variate regression with a novel spectral elastic netregularizer. Further, we show that variational inference for the traceconstrained matrix-variate Gaussian process combined with maximum likelihoodparameter estimation for the bipartite ranking model is jointly convex. Ourmotivating application is the prioritization of candidate disease genes. Thegoal of this task is to aid the identification of unobserved associationsbetween human genes and diseases using a small set of observed associations aswell as kernels induced by gene-gene interaction networks and diseaseontologies. Our experimental results illustrate the performance of the proposedmodel on real world datasets. Moreover, we find that the resulting low ranksolution improves the computational scalability of training and testing ascompared to baseline models.

The trace norm constrained matrix-variate Gaussian process for multitask bipartite ranking