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

Quantitatively predicting phenotype variables by the expression changes in aset of candidate genes is of great interest in molecular biology but it is alsoa challenging task for several reasons. First, the collected biologicalobservations might be heterogeneous and correspond to different biologicalmechanisms. Secondly, the gene expression variables used to predict thephenotype are potentially highly correlated since genes interact though unknownregulatory networks. In this paper, we present a novel approach designed topredict quantitative trait from transcriptomic data, taking into account theheterogeneity in biological samples and the hidden gene regulatory networksunderlying different biological mechanisms. The proposed model performs well onprediction but it is also fully parametric, which facilitates the downstreambiological interpretation. The model provides clusters of individuals based onthe relation between gene expression data and the phenotype, and also leads toinfer a gene regulatory network specific for each cluster of individuals. Weperform numerical simulations to demonstrate that our model is competitive withother prediction models, and we demonstrate the predictive performance and theinterpretability of our model to predict alcohol sensitivity fromtranscriptomic data on real data from Drosophila Melanogaster Genetic ReferencePanel (DGRP).

Nonlinear network-based quantitative trait prediction from transcriptomic data