A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf
.
Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes
[article]
2014
bioRxiv
pre-print
The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants, and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks—graphs with multiple node and edge types—for accomplishing both tasks. First we constructed a
doi:10.1101/011569
fatcat:uwxh3nidtfh37afyj7s2ipn46m