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

Disease-gene prediction (DGP) refers to the computational challenge ofpredicting associations between genes and diseases. Effective solutions to theDGP problem have the potential to accelerate the therapeutic developmentpipeline at early stages via efficient prioritization of candidate genes forvarious diseases. In this work, we introduce the variational graph auto-encoder(VGAE) as a promising unsupervised approach for learning powerful latentembeddings in disease-gene networks that can be used for the DGP problem, thefirst approach using a generative model involving graph neural networks (GNNs).In addition to introducing the VGAE as a promising approach to the DGP problem,we further propose an extension (constrained-VGAE or C-VGAE) which adapts thelearning algorithm for link prediction between two distinct node types inheterogeneous graphs. We evaluate and demonstrate the effectiveness of the VGAEon general link prediction in a disease-gene association network and the C-VGAEon disease-gene prediction in the same network, using popular random walkdriven methods as baselines. While the methodology presented demonstratespotential solely based on utilizing the topology of a disease-gene associationnetwork, it can be further enhanced and explored through the integration ofadditional biological networks such as gene/protein interaction networks andadditional biological features pertaining to the diseases and genes representedin the disease-gene association network.

Towards Probabilistic Generative Models Harnessing Graph Neural Networks for Disease-Gene Prediction