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基于疾病信息网络的表型相似基因搜索
引用本文:侯泳旭,段磊,李岭,卢莉,唐常杰.基于疾病信息网络的表型相似基因搜索[J].软件学报,2018,29(3):721-733.
作者姓名:侯泳旭  段磊  李岭  卢莉  唐常杰
作者单位:四川大学计算机学院, 四川成都 610065,四川大学计算机学院, 四川成都 610065;四川大学华西公共卫生学院, 四川成都 610041,四川大学生命科学学院, 四川成都 610041,四川大学计算机学院, 四川成都 610065,四川大学计算机学院, 四川成都 610065
基金项目:国家自然科学基金项目(61572332,81473446);中国博士后科学基金特别资助项目(2016T90850);中央高校基本科研业务费资助项目(2016SCU04A22)
摘    要:人类基因组计划的成果推动生物信息学研究的发展.基于疾病表型相似性策略寻找功能上存在联系的致病基因,称作"表型相似基因",具有重要的研究价值和广阔的应用前景,成为一个新兴的研究热点.然而,生物医学领域尚没有利用计算机方法开展基于"基因-疾病-表型"关系网络的表型相似基因搜索研究.对此,利用疾病公开数据库构建了包含基因、疾病、表型这三类异构类型节点的疾病信息网络,并设计了基于疾病信息网络的相似基因搜索算法gSim-Miner.针对疾病表型数据的特点,设计了剪枝策略提高算法效率.通过在真实数据上的实验,验证了疾病信息网络对搜索表型相似基因的适用性,以及gSim-Miner算法的有效性,执行效率和可扩展性.

关 键 词:表型相似性  相似基因搜索  疾病信息网络  gSim-Miner
收稿时间:2017/7/31 0:00:00
修稿时间:2017/9/5 0:00:00

Search of Genes with Similar Phenotype Based on Disease Information Network
HOU Yong-Xu,DUAN Lei,LI Ling,LU Li and TANG Chang-Jie.Search of Genes with Similar Phenotype Based on Disease Information Network[J].Journal of Software,2018,29(3):721-733.
Authors:HOU Yong-Xu  DUAN Lei  LI Ling  LU Li and TANG Chang-Jie
Affiliation:School of Computer Science, Sichuan University, Chengdu 610065, China,School of Computer Science, Sichuan University, Chengdu 610065, China;West China School of Public Health, Sichuan University, Chengdu 610041, China,School of Life Science, Sichuan University, Chengdu 610041, China,School of Computer Science, Sichuan University, Chengdu 610065, China and School of Computer Science, Sichuan University, Chengdu 610065, China
Abstract:The results of Human Genome Project promote the development of bioinformatics. Searching disease genes that have function correlations, also called similar phenotype genes, based on the strategy of disease phenome similarity becomes an emerging research topic due to its important research value and wide range of applications. However, in the field of biomedical, there is no previous work to apply computer methods to search similar phenotype genes via a network consists of "gene-disease-phenotype" relations. To fill this gap, a disease information network containing three heterogeneous nodes(i.e., gene, disease, and phenotype) is built by making use of a disease open database. In addition, an algorithm, called gSim-Miner, is designed for the search of similar phenotype genes via the disease information network. Pruning strategies based on the characteristics of disease phenotype data are proposed to improve the efficiency of gSim-Miner. Experiments on real-world data sets demonstratethat the disease information network is applicative,and gSim-Mineris effective,efficient and extensible.
Keywords:phenotype similarity  search of similar genes  disease information network  gSim-Miner
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