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

A unique challenge in predictive model building for omics data has been thesmall number of samples $(n)$ versus the large amount of features $(p)$. This"$n\ll p$" property brings difficulties for disease outcome classificationusing deep learning techniques. Sparse learning by incorporating external genenetwork information such as the graph-embedded deep feedforward network (GEDFN)model has been a solution to this issue. However, such methods require anexisting feature graph, and potential mis-specification of the feature graphcan be harmful on classification and feature selection. To address thislimitation and develop a robust classification model without relying onexternal knowledge, we propose a \underline{for}est\underline{g}raph-\underline{e}mbedded deep feedforward \underline{net}work(forgeNet) model, to integrate the GEDFN architecture with a forest featuregraph extractor, so that the feature graph can be learned in a supervisedmanner and specifically constructed for a given prediction task. To validatethe method's capability, we experimented the forgeNet model with both syntheticand real datasets. The resulting high classification accuracy suggests that themethod is a valuable addition to sparse deep learning models for omics data.

forgeNet: A graph deep neural network model using tree-based ensemble classifiers for feature extraction