[1]李建伟,等.基于异构网络拓扑数据的人类必要基因预测[J].河北工业大学学报,2018,(03):36-41.[doi:10.14081/j.cnki.hgdxb.2018.03.006]
 LI Jianwei,YUE Zonghe,et al.Human essential gene prediction based on heterogeneous network topology data[J].Journal of Hebei University of Technology,2018,(03):36-41.[doi:10.14081/j.cnki.hgdxb.2018.03.006]
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基于异构网络拓扑数据的人类必要基因预测
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《河北工业大学学报》[ISSN:1007-2373/CN:13-1208/T]

卷:
期数:
2018年03期
页码:
36-41
栏目:
计算机应用
出版日期:
2018-06-25

文章信息/Info

Title:
Human essential gene prediction based on heterogeneous network topology data
文章编号:
1007-2373(2018)03-0036-06
作者:
李建伟1 2岳宗河1黄 焱1段向欢1 
(1.河北工业大学 人工智能与数据科学学院,天津 300401;2.河北省大数据计算重点实验室,天津 300401)
Author(s):
LI Jianwei1 2YUE Zonghe1HUANGYan1DUAN Xianghuan1
(1. School ofArtificial Intelligence, Hebei University of Technology, Tianjin 300401, China;2. Hebei Province Key Laboratory of
Big Data Calculation, Tianjin 300401, China)

关键词:
人类必要基因异构网络过抽样重启动随机游走支持向量机
Keywords:
humanessentialgenesheterogeneousnetworksoversamplingrandomwalkwithrestartalgorithm supportvectormachine
分类号:
TP301.6
DOI:
10.14081/j.cnki.hgdxb.2018.03.006
文献标志码:
A
摘要:
对必要基因进行研究不仅能够了解生物生存和繁殖的最低要求,且有助于寻找人类疾病基因和新的药 物靶点. 实验法鉴定人类必要基因虽有效但价格昂贵且耗时费力,开发高效算法预测必要基因是对实验法必要 而有效的补充. 提出一种基于融合多个异构网络拓扑数据预测必要基因的算法,该算法选用重启动随机游走算 法将多个异构网络整合成统一的基因网络特征,采用SMOTE过抽样算法平衡训练支持向量机过程中的正负样 本. 实验结果表明,整合异构网络拓扑数据方法比基于单一网络的模型能更有效地预测人类必要基因. 
Abstract:
Thestudiesoftheessentialgenesarehelpfulnotonlyinunderstandingtheminimumrequirementsforsurviv? alandreproduction, butalsofindingthenewhumandiseasegenesanddrugtargets. Thoughtheexperimentalmethodsto identifytheessentialgenesiseffective, thesemethodsareexpensiveandtime-consuming.Therefore, thedevelopmentof efficientpredictionalgorithmtopredicthumanessentialgenesisanecessaryandeffectivecomplementtoexperimental methods.Thispaperproposedanalgorithmbasedonthefusionofmultipleheterogeneousnetworktopologydatatopredict theessentialgenes. Inourstudy, randomwalkwithrestartalgorithmwasusedtointegrateheterogeneousnetworktopo? logicaldataintouniformednetworkfeaturesofgenes. SMOTEoversamplingalgorithmwasadoptedtobalancetheposi? tiveandnegativesamplesintrainingSVM.Theexperimentalresultsshowthatthemethodofintegratingheterogeneous networktopologydatacanpredicthumanessentialgenesmoreeffectivelythanthosebasedthesinglenetworkmodel. 

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-03-06 基金项目:国家自然科学基金(81672113) 作者简介:李建伟(1974—),男,教授,lijianwei@hebut.edu.cn.


更新日期/Last Update: 2018-07-12