A network flow approach to predict drug targets from microarray data, disease genes and interactome network - case study on prostate cancer.
A network flow approach to predict drug targets from microarray data, disease genes and interactome network - case study on prostate cancer.
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DOI：
10.1186/2043-9113-2-1
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发表时间：
2012-01-13
期刊：
影响因子：
--
通讯作者：
Soo VW
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文献类型：
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作者：
Yeh SH;Yeh HY;Soo VW
药物发现的系统方法是系统生物学研究领域的一门新兴学科。它旨在整合相互作用数据和实验数据来阐明疾病，并提出了癌症治疗药物发现的新问题。然而，药物靶点的发现仍处于试错实验阶段，开发一种能够系统地检测可能的药物靶点以应对复杂疾病的预测模型是一项具有挑战性的任务。我们将基因表达、疾病基因和相互作用网络结合起来，利用网络流方法来识别对疾病基因有强烈影响的有效药物靶点。在实验中，我们采用包含62个前列腺癌样本和41个正常样本的微阵列数据集，从DrugBank数据库中提取的108个已知前列腺癌基因和322个批准的药物靶标作为候选蛋白作为我们的测试数据。使用我们的方法，我们优先考虑候选蛋白质，并将其验证为已知的前列腺癌药物靶标。我们成功地确定了与已知的前列腺癌治疗药物密切相关的潜在药物靶点，并发现了更多的潜在药物靶点，引起了目前生物学家的关注。我们指出，这是很难发现的药物靶标仅基于差异表达的变化，由于事实上，这些基因用于药物靶标可能并不总是有显着的表达变化。与以往依赖于网络拓扑属性的方法相比，它们证明了具有潜在药物靶点的基因与网络中的临界点之间的弱相关性。与以前的方法相比，我们的结果具有最高的平均平均精度，也排名的位置，真正的药物靶点更高。从而验证了该方法的有效性。我们的方法不知道疾病网络中的真实的理想路径，但它试图找到可行的流，通过可能的路径对疾病基因产生强烈的影响。我们成功地将药物靶点预测的识别制定为生物网络上的最大流问题，并以准确的方式发现潜在的药物靶点。
Systematic approach for drug discovery is an emerging discipline in systems biology research area. It aims at integrating interaction data and experimental data to elucidate diseases and also raises new issues in drug discovery for cancer treatment. However, drug target discovery is still at a trial-and-error experimental stage and it is a challenging task to develop a prediction model that can systematically detect possible drug targets to deal with complex diseases. We integrate gene expression, disease genes and interaction networks to identify the effective drug targets which have a strong influence on disease genes using network flow approach. In the experiments, we adopt the microarray dataset containing 62 prostate cancer samples and 41 normal samples, 108 known prostate cancer genes and 322 approved drug targets treated in human extracted from DrugBank database to be candidate proteins as our test data. Using our method, we prioritize the candidate proteins and validate them to the known prostate cancer drug targets. We successfully identify potential drug targets which are strongly related to the well known drugs for prostate cancer treatment and also discover more potential drug targets which raise the attention to biologists at present. We denote that it is hard to discover drug targets based only on differential expression changes due to the fact that those genes used to be drug targets may not always have significant expression changes. Comparing to previous methods that depend on the network topology attributes, they turn out that the genes having potential as drug targets are weakly correlated to critical points in a network. In comparison with previous methods, our results have highest mean average precision and also rank the position of the truly drug targets higher. It thereby verifies the effectiveness of our method. Our method does not know the real ideal routes in the disease network but it tries to find the feasible flow to give a strong influence to the disease genes through possible paths. We successfully formulate the identification of drug target prediction as a maximum flow problem on biological networks and discover potential drug targets in an accurate manner.