东南大学学报（自然科学版）2016，Vol.46Issue(2)：265-270,6.DOI:10.3969/j.issn.1001-0505.2016.02.007
基于关键节点子团的乳腺癌候选疾病模块挖掘算法
Mining algorithm for breast cancer candidate disease module based on key node groups
摘要
Abstract
In order to solve the problems of small quantity, incomplete data, noise, and bias of the gene expression profile in the method for breast cancer disease module mining, a mining algorithm for candidate disease module based on the key node groups and the local node fitness constraints, the key node groups and local fitness ( KNGLF) algorithm, is proposed.First, the topological overlap similarity score and the functional similarity score between the candidate genes and the pathogenic genes are fused into a fusion score.Through comparing the fusion score with the threshold value, the key nodes are selected and the key node groups are constructed.Then, the breast cancer candi-date disease modules are mined based on the local fitness constraints and different decision criteria for different nodes.Finally, according to the enrichment analysis results, the candidate disease gene modules are identified.The experimental results show that compared with other existing mining algorithms for breast cancer module, the key node selection algorithm in the KNGLF algorithm has the smaller MRR ( mean rank ratio) but the greater AUC ( area under curve) .Fifteen breast canc-er candidate gene modules with significant biological significance are identified by the KNGLF algorithm.Besides, the KNGLF algorithm can be extended to identify other diseases related candi-date modules.关键词
乳腺癌/疾病模块挖掘/候选基因打分/关键节点子团/局部适应度Key words
breast cancer/disease module mining/candidate gene score/key node groups/local fitness分类
基础医学引用本文复制引用
王一斌,程咏梅,张绍武..基于关键节点子团的乳腺癌候选疾病模块挖掘算法[J].东南大学学报（自然科学版）,2016,46(2):265-270,6.基金项目
国家自然科学基金资助项目（61170134，61473232，91430111）、国家自然科学基金青年基金资助项目（61502396）、互联网金融创新及监管四川省协同创新中心资助项目． （）
