基于再生核希尔伯特空间的In-Silico基因网络重构  

Reconstructing In-Silico Gene Regulatory Network Based on Reproducing Kernel Hilbert Space

作　　者：金丽艳[1] 韩斌[1] 厉力华[1] 祝磊[1] 樊双喜[1] 

机构地区：[1]杭州电子科技大学生命信息与仪器工程学院,杭州310018

出　　处：《生物物理学报》2013年第7期515-526,共12页Acta Biophysica Sinica

基　　金：国家自然科学基金项目(61271063);"973"计划项目(2013CB329502);国家杰出青年科学基金项目(60788101)~~

摘　　要：针对逆向工程的评估与方法交流(Dialogue for Reverse Engineering Assessments and Methods,DREAM)第四次竞赛(DREAM4)中In-Silico基因调控网络(Challenge2)的重构问题,作者提出一种基于再生核希尔伯特空间的统计独立性度量方法(Hilbert-Schmidt independence criterion,HSIC)。该方法不要求数据符合某种特定的分布,约束条件少,是一种非参数计算统计独立性的方法。对10规模的In-Silico基因网络,HSIC方法的受试者工作特征曲线面积(area under receiver operating characteristic curve,AUROC)比常微分方程(ordinary differential equation,ODE)方法和格兰杰因果关系(granger causality,GC)方法分别高了16%和7%,比动态贝叶斯网络(dynamic bayesian network,DBN)方法和非线性动态系统(nonlinear dynamic systems,NDS)方法中的最好算法分别高了2.4%和1.4%。对100规模的In-Silico基因网络,HSIC方法的AUROC分别超出ODE及GC方法 16%和14.2%,超出DBN和NDS方法中的最好算法5%和1.4%。实验表明,HSIC方法具有基因调控网络重构的可行性与可靠性,并且对In-Silico网络的重构准确率要优于目前经典的基因调控网络建模方法。In the fourth Dialogue for Reverse Engineering Assessments and Methods （DREAM4） competition, In-Silico dataset （Challenge 2） was generated with a ＇true＇ biological gene network. The aim of this work is to reconstruct gene network structure from the data. Here, the authors presented a statistical independent measurement method based on reproducing kernel Hilbert space- Hilbert-Schmidt independence criteria （HSIC） to identify the gene regulatory network. Instead of data fitting, HSIC provides a criterion to measure the statistical dependence. Besides, it is a nonparametric method, has no assumption on the data distribution and computationally efficient. Comparative experimental results showed that the HSIC achieved a better performance than several classical gene regulatory network modeling methods. For size 10 network, the area under receiver operating characteristic curve （AUROC） value obtained by HSIC was 16 percent higher than ordinary differential equations （ODE）, 7 percent higher than granger causality （GC）, 2.4 percent and 1.4 percent higher than the best algorithm in dynamic Bayesian network （DBN） and nonlinear dynamical systems （NDS）, respectively. For size 100 network, the AUROC value of HSIC was 16 percent higher than ODE, 14.2 percent higher than GC, 5 percent and 1.4 percent higher than the best algorithm in DBN and NDS. These results reveal that the HSIC method has stronger capability of structural identification and is feasible for construct complex gene regulatory network.

关 键 词：基因调控网络 重构 再生核希尔伯特空间 独立性 受试者工作特征曲线面积 

分 类 号：R318[医药卫生—生物医学工程]