A phenome-guided drug repositioning through a latent variable model.
A phenome-guided drug repositioning through a latent variable model.
复制标题
DOI：
10.1186/1471-2105-15-267
复制
发表时间：
2014-08-08
影响因子：
3
通讯作者：
Tong W
中科院分区：
文献类型：
--
作者：
Bisgin H;Liu Z;Fang H;Kelly R;Xu X;Tong W
这种现象代表了人类群体中一组独特的信息。人们特别探索了它与基因组的关系，以确定疾病的相关性。该现象也被用于药物重新定位，重点是寻找最相似的候选药物的搜索空间。为了对表型进行全面分析，我们假设所有表型（适应症和副作用）都与概率分布相互关联，这一特征可能为确定给定药物的新治疗适应症提供机会。相应地，我们采用了潜在狄利克雷分配（LDA），它引入了潜在变量（主题）来控制现象分布。我们根据副作用资源（Side Effect Resource， SIDER）中的现象组信息建立了我们的模型。我们首先开发了一个基于恢复潜力的LDA模型，通过对每个药物适应症对的药物表型矩阵进行扰动，使每个药物适应症关系切换到“未知”的关系，然后基于剩余的药物表型对进行恢复。在概率显著的对中，70%被成功恢复。接下来，我们将该模型应用于整个现象，以缩小重新定位的候选对象并提出替代适应症。我们能够检索到6种未在SIDER中列出的适应症的药物的批准适应症。对于908种存在其适应症信息的药物，我们的模型建议了进一步研究的替代治疗方案。一些建议的新用途可以从科学文献中得到支持。结果表明，该现象可以通过生成模型进一步分析，该模型可以发现药物和治疗用途之间的概率关联。在这方面，LDA作为一种浓缩工具，通过缩小搜索空间来探索现有药物的新用途。本文的在线版本（doi:10.1186/1471-2105-15-267）包含补充材料，可供授权用户使用。
The phenome represents a distinct set of information in the human population. It has been explored particularly in its relationship with the genome to identify correlations for diseases. The phenome has been also explored for drug repositioning with efforts focusing on the search space for the most similar candidate drugs. For a comprehensive analysis of the phenome, we assumed that all phenotypes (indications and side effects) were inter-connected with a probabilistic distribution and this characteristic may offer an opportunity to identify new therapeutic indications for a given drug. Correspondingly, we employed Latent Dirichlet Allocation (LDA), which introduces latent variables (topics) to govern the phenome distribution. We developed our model on the phenome information in Side Effect Resource (SIDER). We first developed a LDA model optimized based on its recovery potential through perturbing the drug-phenotype matrix for each of the drug-indication pairs where each drug-indication relationship was switched to “unknown” one at the time and then recovered based on the remaining drug-phenotype pairs. Of the probabilistically significant pairs, 70% was successfully recovered. Next, we applied the model on the whole phenome to narrow down repositioning candidates and suggest alternative indications. We were able to retrieve approved indications of 6 drugs whose indications were not listed in SIDER. For 908 drugs that were present with their indication information, our model suggested alternative treatment options for further investigations. Several of the suggested new uses can be supported with information from the scientific literature. The results demonstrated that the phenome can be further analyzed by a generative model, which can discover probabilistic associations between drugs and therapeutic uses. In this regard, LDA serves as an enrichment tool to explore new uses of existing drugs by narrowing down the search space. The online version of this article (doi:10.1186/1471-2105-15-267) contains supplementary material, which is available to authorized users.
登录
查看更多内容
影响因子：
3.2
作者：
De Deyn, Peter Paul;Drenth, Annemieke F. J.;Van Dam, Debby
通讯作者：
Van Dam, Debby
影响因子：
8.8
作者：
Ibrahim, AE;Ghoneim, MM;Philip, BK
通讯作者：
Philip, BK
影响因子：
4.2
作者：
Morita, Kyoji;Gotohda, Takako;Her, Song
通讯作者：
Her, Song
影响因子：
56.9
作者：
Campillos, Monica;Kuhn, Michael;Bork, Peer
通讯作者：
Bork, Peer
影响因子：
3
作者：
Bisgin H;Liu Z;Kelly R;Fang H;Xu X;Tong W
通讯作者：
Tong W