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Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation

机译：使用网络传播从化学，基因组和表型数据的整合中推断药物-疾病关联

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Background During the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue. Methods We combine three different networks of drug, genomic and disease phenotype and assign the weights to the edges from available experimental data and knowledge. Given a specific disease, we use our network propagation approach to infer the drug-disease associations. Results We apply prostate cancer and colorectal cancer as our test data. We use the manually curated drug-disease associations from comparative toxicogenomics database to be our benchmark. The ranked results show that our proposed method obtains higher specificity and sensitivity and clearly outperforms previous methods. Our result also show that our method with off-targets information gets higher performance than that with only primary drug targets in both test data. Conclusions We clearly demonstrate the feasibility and benefits of using network-based analyses of chemical, genomic and phenotype data to reveal drug-disease associations. The potential associations inferred by our method provide new perspectives for toxicogenomics and drug reposition evaluation.
机译：背景技术在过去的几年中，药物，疾病表型和蛋白质的知识已迅速积累，越来越多的科学家引起了人们对通过计算方法推断药物-疾病关联的关注。通过这些信息数据开发系统发现药物-疾病关联的综合方法是一个重要的问题。方法我们结合药物，基因组和疾病表型的三种不同网络，并根据可用的实验数据和知识将权重分配给边缘。给定特定疾病，我们使用网络传播方法来推断药物-疾病关联。结果我们将前列腺癌和结肠直肠癌作为我们的测试数据。我们使用来自比较毒理基因组学数据库的人工策划的药物-疾病关联作为我们的基准。排名结果表明，我们提出的方法具有更高的特异性和灵敏度，并且明显优于以前的方法。我们的结果还表明，在两个测试数据中，具有脱靶信息的方法比仅具有主要药物靶的方法具有更高的性能。结论我们清楚地证明了使用基于网络的化学，基因组和表型数据分析来揭示药物-疾病关联的可行性和益处。通过我们的方法推断的潜在关联为毒理基因组学和药物定位评估提供了新的视角。

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