Identifying gene-drug patterns is a critical step in pharmacology for unveiling disease mechanisms and drug discovery. The availability of high-throughput technologies accumulates massive large-scale pharmacological and genomic data, and thus provides a new substantial opportunity to deeply understand how the oncogenic genes and the therapeutic drugs relate to each other. However, most previous studies merely used the pharmacological and genomic datasets without any prior knowledge to infer the gene-drug patterns. Here, we proposed a novel network-guided sparse binary matching model (NSBM) to decode these relationships hidden in the datasets. Not only the large-scale gene-expression data and drug-response data are jointly analyzed in our method, but also the additional prior information of genes and drugs are integrated into the form of network-based regularization. The essential structure of the NSBM model is a convex quadratic minimization problem with network-based penalties. It was demonstrated to be superior when compared with two benchmark methods through extensive experiments on both synthetic and empirical data. Posterior validation, including gene-ontology and enrichment analysis, confirmed the effectiveness of NSBM in revealing gene-drug patterns on a large-scale heterogeneous data source.

中文翻译：

---

通过稀疏二进制匹配识别基因表达数据和药物反应数据之间的“多对多”关系。

鉴定基因药物模式是药理学揭示疾病机理和药物发现的关键步骤。高通量技术的可用性积累了大量的大规模药理和基因组数据，从而为深入了解致癌基因与治疗药物之间的相互关系提供了新的实质性机会。但是，大多数先前的研究仅使用药理学和基因组数据集，而没有任何先验知识来推断基因药物模式。在这里，我们提出了一种新颖的网络引导的稀疏二进制匹配模型（NSBM）来解码隐藏在数据集中的这些关系。我们的方法不仅可以共同分析大规模的基因表达数据和药物反应数据，而且还将基因和药物的其他先验信息整合到基于网络的正则化形式中。NSBM模型的基本结构是具有基于网络的惩罚的凸二次最小化问题。通过对合成数据和经验数据进行广泛的实验，与两种基准方法相比，它具有优越性。后验验证，包括基因本体论和富集分析，证实了NSBM在大规模异质数据源上揭示基因药物模式的有效性。