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 Statement The authors have declared no competing interest.
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