基于多关联数据融合的疾病相似度算法研究

项目名称： 基于多关联数据融合的疾病相似度算法研究

项目编号： No.61502125

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 计算机科学学科

项目作者： 程亮

作者单位： 哈尔滨医科大学

项目金额： 20万元

中文摘要： 疾病相似度对于研究与疾病相关的分子功能有非常重要的作用。当前，疾病对的文献、基因以及语义关联数据常被用于计算疾病相似度。尽管每种关联数据都从不同的角度反映了疾病的相似性，但是现有的方法并没有综合的利用所有的关联数据。为了更加全面地理解疾病相似度，本项目致力于融合所有的关联数据。本项目的主要研究内容包括以下三部分：1）设计多关联数据融合的疾病相似度方法。首先，比较真实的关联关系与重新排列的关联关系在已有的方法下的疾病相似性，基于每种类型的关联关系得到一个疾病相似性的假阳性率；然后通过Fisher联合概率检验得到疾病相似性的P-value。2）基于ROC（Receiver Operating Characteristic）曲线评估方法的性能，并验证方法符合假设“相似的疾病可以被同样的药物治疗”。3）预测疾病的潜在治疗药物、挖掘并排序疾病间的关联路径，进而构建疾病相似度计算与分析系统。

中文关键词： 疾病研究；生物信息学；疾病相似度；多关联数据融合；潜在治疗药物

英文摘要： Measuring similarity between diseases plays an important role in disease-related molecular function research. Currently, literature, gene, and semantic association data between diseases is often used to calculate disease similarity. Disease similarity can be reflected by each type of association data from different view. However, not all of these association data is considered in existing methods. In order to comprehensively understand disease similarity, we focus on fusing multi-association data. The main studies include: 1) Method for calculating disease similarity is proposed by fusing multi-association data. First, disease similarities based on existing methods are compared using real associations and permutated associations, and false discovery rate (FDR) of disease similarity from each type of association can be accessed. Then, P-value of the disease similarity can be obtained using Fisher's combined probability test. 2) Receiver Operating Characteristic (ROC) curve is exploited to access the performance. In addition, the method is validated using the hypothesis that similar diseases can be treated by the same drugs. 3) Potential therapeutic drugs of disease are predicted, and coherent paths between diseases are mined and prioritized, and then the calculation and analysis system of disease similarity is established.

英文关键词： disease research;bioinfomatics;disease similarity;multi-association data fusion;potential therapeutic drugs