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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Elucidating the genetic basis of human diseases is a central goal of geneticsand molecular biology. While traditional linkage analysis and modernhigh-throughput techniques often provide long lists of tens or hundreds ofdisease gene candidates, the identification of disease genes among thecandidates remains time-consuming and expensive. Efficient computationalmethods are therefore needed to prioritize genes within the list of candidates,by exploiting the wealth of information available about the genes in variousdatabases. Here we propose ProDiGe, a novel algorithm for Prioritization ofDisease Genes. ProDiGe implements a novel machine learning strategy based onlearning from positive and unlabeled examples, which allows to integratevarious sources of information about the genes, to share information aboutknown disease genes across diseases, and to perform genome-wide searches fornew disease genes. Experiments on real data show that ProDiGe outperformsstate-of-the-art methods for the prioritization of genes in human diseases.

ProDiGe: PRioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples