基于双层耦合网的表型-基因关联分析与预测

随着基因组测序完成和基因技术不断发展，使得某些疾病的致病基因逐渐得到确认。目前，通过科学实验已经掌握了一部分疾病的致病原因，但是大部分疾病的致病原因，特别是与基因相关的疾病的致病原因还不得而知。该文采用与人类同源相似度高达85%的小鼠数据作为研究对象，使用疾病表型数据集、致病基因数据集和已经确认的表型?基因关联关系数据集构成一个双层耦合网络，通过元路径上随机游走的方法进行数据的分析与挖掘，在已经确认的表型?基因关联数据基础上预测未确定的表型?基因关联关系。经验证比较，该文提出的算法所取得的预测效果优于其他算法。

Phenotype-Gene Association Analysis and Prediction Based on Double-Layer Coupled Network

With the completion of genome sequencing and the continuous development of gene technology, the pathogenic genes of some diseases are gradually identified. At present, people have grasped the pathogenic causes of some diseases through scientific experiments, but the pathogenic causes of most diseases, especially those related to genes, are still unknown. In this paper, the mouse data with 85% homology similarity to human is used as the research object. The disease phenotype data set, pathogenic gene data set and confirmed phenotype-gene association data set are constructed into a double-layer coupled network. The data are analyzed and mined by meta-path random walk method, and the uncertainties are predicted on the basis of confirmed phenotype-gene association data. The proposed algorithm achieves better prediction results compared with other algorithms.