Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation.
Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation.
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DOI：
10.1186/1755-8794-6-s3-s4
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发表时间：
2013
影响因子：
2.7
通讯作者：
Soo VW
中科院分区：
文献类型：
--
作者：
Huang YF;Yeh HY;Soo VW
近年来，关于药物、疾病表型和蛋白质的知识迅速积累，越来越多的科学家开始关注用计算方法推断药物与疾病的关联。开发一种综合方法，通过这些信息数据系统地发现药物与疾病之间的联系是一个重要问题。我们结合了药物、基因组和疾病表型三种不同的网络，并从可用的实验数据和知识中为边缘分配权重。给定一个特定的疾病，我们使用我们的网络传播方法来推断药物-疾病的关联。我们使用前列腺癌和结直肠癌作为我们的测试数据。我们使用比较毒物基因组学数据库中人工整理的药物-疾病关联作为基准。排序结果表明，本文提出的方法具有更高的特异性和敏感性，明显优于以往的方法。我们的结果还表明，我们的方法在两个测试数据中都具有非靶点信息，比仅使用主要药物靶点的方法具有更高的性能。我们清楚地展示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。我们的方法推断的潜在关联为毒物基因组学和药物重新定位评估提供了新的视角。
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. 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. 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. 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.