Mining drug targets and mechanisms of action (MoA) for novel anticancer drugs from pharmacogenomic data is a path to enhance the drug discovery efficiency. Recent approaches have successfully attempted to discover targets/MoA by characterizing drug similarities and communities with integrative methods on multi-modal or multi-omics drug information. However, the sparse and imbalanced community size structure of the drug network is seldom considered in recent approaches. Consequently, we developed a novel network integration approach accounting for network structure by a reciprocal nearest neighbor and contextual information encoding (RNCE) approach. In addition, we proposed a tailor-made clustering algorithm to perform drug community detection on drug networks. RNCE and spectral clustering are proved to outperform state-of-the-art approaches in a series of tests, including network similarity tests and community detection tests on two drug databases. The observed improvement of RNCE can contribute to the field of drug discovery and the related multi-modal/multi-omics integrative studies. Availabilityhttps://github.com/WINGHARE/RNCE.

中文翻译：

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RNCE：用于癌症靶点多模式药物社区研究的互惠邻居上下文编码的网络集成。

从药物基因组数据中挖掘新型抗癌药物的药物靶点和作用机制（MoA）是提高药物发现效率的途径。最近的方法已经成功地尝试通过使用多模式或多组学药物信息的综合方法表征药物相似性和社区来发现目标/MoA。然而，最近的方法很少考虑药物网络的稀疏和不平衡的社区规模结构。因此，我们开发了一种新颖的网络集成方法，通过互易最近邻和上下文信息编码 (RNCE) 方法来解释网络结构。此外，我们提出了一种量身定制的聚类算法来对毒品网络进行毒品社区检测。RNCE 和谱聚类在一系列测试中被证明优于最先进的方法，包括对两个药物数据库的网络相似性测试和社区检测测试。观察到的 RNCE 的改进有助于药物发现领域和相关的多模式/多组学综合研究。一个vailability https://github.com/WINGHARE/RNCE。