RT Journal Article
SR Electronic
T1 Genome-wide discovery of hidden genes mediating known drug-disease association
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 749762
DO 10.1101/749762
A1 Hua Yu
A1 Lu Lu
A1 Ming Chen
A1 Chen Li
A1 Jin Zhang
YR 2020
UL http://biorxiv.org/content/early/2020/09/25/749762.abstract
AB Identifying of hidden genes mediating Known Drug-Disease Association (KDDA) is of great significance for understanding disease pathogenesis and guiding drug repurposing. Here, we present a novel computational tool, called KDDANet, for systematic and accurate uncovering hidden genes mediating KDDA from the perspective of genome-wide gene functional interaction network. By implementing minimum cost flow optimization, combined with depth first searching and graph clustering on a unified flow network model, KDDANet outperforms existing methods in both sensitivity and specificity of identifying genes in mediating KDDA. Case studies on Alzheimer’s disease (AD) and obesity uncover the mechanistic relevance of KDDANet predictions. Furthermore, when applied with multiple types of cancer-omics datasets, KDDANet not only recapitulates known genes mediating KDDAs related to cancer, but also uncovers novel candidates that offer new biological insights. Importantly, KDDANet can be used to discover the shared genes mediating multiple KDDAs. KDDANet can be accessed at http://www.kddanet.cn and the code can be freely downloaded at https://github.com/huayu1111/KDDANet/.Competing Interest StatementThe authors have declared no competing interest.