Hansen N T, Brunak S, Altman R B
Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark.
Clin Pharmacol Ther. 2009 Aug;86(2):183-9. doi: 10.1038/clpt.2009.42. Epub 2009 Apr 15.
A critical task in pharmacogenomics is identifying genes that may be important modulators of drug response. High-throughput experimental methods are often plagued by false positives and do not take advantage of existing knowledge. Candidate gene lists can usefully summarize existing knowledge, but they are expensive to generate manually and may therefore have incomplete coverage. We have developed a method that ranks 12,460 genes in the human genome on the basis of their potential relevance to a specific query drug and its putative indications. Our method uses known gene-drug interactions, networks of gene-gene interactions, and available measures of drug-drug similarity. It ranks genes by building a local network of known interactions and assessing the similarity of the query drug (by both structure and indication) with drugs that interact with gene products in the local network. In a comprehensive benchmark, our method achieves an overall area under the curve of 0.82. To showcase our method, we found novel gene candidates for warfarin, gefitinib, carboplatin, and gemcitabine, and we provide the molecular hypotheses for these predictions.
药物基因组学中的一项关键任务是识别可能是药物反应重要调节因子的基因。高通量实验方法常常受到假阳性的困扰，并且没有利用现有知识。候选基因列表可以有效地总结现有知识，但手动生成它们成本高昂，因此可能覆盖不完整。我们开发了一种方法，该方法基于人类基因组中的12460个基因与特定查询药物及其假定适应症的潜在相关性对它们进行排名。我们的方法使用已知的基因-药物相互作用、基因-基因相互作用网络以及可用的药物-药物相似性度量。它通过构建已知相互作用的局部网络并评估查询药物（通过结构和适应症）与在局部网络中与基因产物相互作用的药物的相似性来对基因进行排名。在一项全面的基准测试中，我们的方法实现了曲线下面积为0.82。为了展示我们的方法，我们找到了华法林、吉非替尼、卡铂和吉西他滨的新基因候选物，并为这些预测提供了分子假说。
