ANTENNA, a Multi-Rank, Multi-Layered Recommender System for Inferring Reliable Drug-Gene-Disease Associations: Repurposing Diazoxide as a Targeted Anti-Cancer Therapy.
ANTENNA, a Multi-Rank, Multi-Layered Recommender System for Inferring Reliable Drug-Gene-Disease Associations: Repurposing Diazoxide as a Targeted Anti-Cancer Therapy.
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DOI：
10.1109/tcbb.2018.2812189
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发表时间：
2018-11
期刊：
影响因子：
--
通讯作者：
Xie L
中科院分区：
文献类型：
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作者：
Wang A;Lim H;Cheng SY;Xie L
现有的药物发现过程遵循“一药一基因一疾病”的简化主义模式，不足以解决涉及多个功能失调基因的复杂疾病。大组学数据的可用性为将药物发现过程转变为系统药理学的新范式提供了机会，该范式侧重于针对分子相互作用网络而不是单个基因设计药物。在这里，我们开发了一个可靠的多层推荐系统ANTENNA，用于挖掘大规模化学基因组学和疾病关联数据，以预测新型药物-基因-疾病关联。天线集成了一种新的基于三因子分解的双正则化加权和输入的一类协同滤波（OCCF）算法tREMAP，以及基于随机行走与重启的统计框架，并评估特定预测的可靠性。在基准测试中，tREMAP明显优于单列OCCF。我们将ANTENNA应用于一个现实世界的问题：在没有有效治疗的情况下，将旧药物用于新的临床适应症。我们发现fda批准的药物二氮氧化合物可以抑制多种疾病的激酶基因，包括癌症，并有效杀死三阴性乳腺癌（TNBC）细胞（IC50 = 0.87 μM）。TNBC是一种致命的疾病，没有有效的靶向治疗。我们的发现证明了大数据分析在药物发现和开发针对TNBC的靶向治疗方面的力量。
Existing drug discovery process follows a reductionist model of “one-drug-one-gene-one-disease,” which is inadequate to tackle complex diseases involving multiple malfunctioned genes. The availability of big omics data offers opportunities to transform drug discovery process into a new paradigm of systems pharmacology that focuses on designing drugs to target molecular interaction networks instead of a single gene. Here, we develop a reliable multi-rank, multi-layered recommender system, ANTENNA, to mine large-scale chemical genomics and disease association data for prediction of novel drug-gene-disease associations. ANTENNA integrates a novel tri-factorization based dual-regularized weighted and imputed One Class Collaborative Filtering (OCCF) algorithm, tREMAP, with a statistical framework based on Random Walk with Restart and assess the reliability of specific predictions. In the benchmark, tREMAP clearly outperforms the single-rank OCCF. We apply ANTENNA to a real-world problem: repurposing old drugs for new clinical indications without effective treatments. We discover that FDA-approved drug diazoxide can inhibit multiple kinase genes responsible for many diseases including cancer and kill triple negative breast cancer (TNBC) cells efficiently (IC50 = 0.87 μM). TNBC is a deadly disease without effective targeted therapies. Our finding demonstrates the power of big data analytics in drug discovery and developing a targeted therapy for TNBC.