[1]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.[doi:10.3969/j.issn.1001-0505.2016.02.007]
 Wang Yibin,Cheng Yongmei,Zhang Shaowu.Mining algorithm for breast cancer candidate disease module based on key node groups[J].Journal of Southeast University (Natural Science Edition),2016,46(2):265-270.[doi:10.3969/j.issn.1001-0505.2016.02.007]
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基于关键节点子团的乳腺癌候选疾病模块挖掘算法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
46
期数:
2016年第2期
页码:
265-270
栏目:
生物医学工程
出版日期:
2016-03-20

文章信息/Info

Title:
Mining algorithm for breast cancer candidate disease module based on key node groups
作者:
王一斌程咏梅张绍武
西北工业大学信息融合技术教育部重点实验室, 西安 710072
Author(s):
Wang Yibin Cheng Yongmei Zhang Shaowu
Key Laboratory of Information Fusion Technology of Ministry of Education, Northwestern Polytechnical University, Xi’an 710072, China
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
Keywords:
breast cancer disease module mining candidate gene score key node groups local fitness
分类号:
R318.04;Q78
DOI:
10.3969/j.issn.1001-0505.2016.02.007
摘要:
为解决乳腺癌疾病模块挖掘方法中基因表达谱样本数量少、数据不完整、存在噪声和偏差的问题,提出了一种基于关键节点子团和局部适应度的候选疾病模块挖掘算法——KNGLF算法.该算法首先将候选基因与致病基因间的重叠相似性得分和功能相似性得分进行融合,通过比较融合得分与阈值,筛选出关键节点,并构建关键节点子团;然后,基于局部适应度及不同节点对应的不同判定标准,扩展挖掘候选疾病模块;最后,根据富集分析结果确定候选疾病基因模块.实验结果表明,与现有其他乳腺癌模块挖掘算法相比,KNGLF中关键节点选择算法所得平均排名较小,曲线下面积较大.KNGLF算法挖掘出15个具有较显著生物意义的乳腺癌候选疾病模块.此外,KNGLF算法还可扩展至其他疾病候选模块.
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 candidate 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 cancer 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 candidate modules.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2015-07-15.
作者简介: 王一斌(1982—),男,博士生;张绍武(联系人),男,博士,教授,博士生导师,zhangsw@nwpu.edu.cn.
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.
引用本文: 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. DOI:10.3969/j.issn.1001-0505.2016.02.007.
更新日期/Last Update: 2016-03-20