代谢组学数据分析的统计学方法(4)

发布时间:2021-06-06

ChineseJournalofHealthStatistics,Apr2014,Vol.31,No.2·365·

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])+ya[i-1,]);temp=T(hlast*(0:nints[i-1,f=j(nrow(temp),ncol(x),0);dow=1tonrow(temp);dov=1toncol(x);

f[w,v]=last[w,]*pdf('normal',temp[w,],x[,v],stdv[i,]);end;end;

last=t(0.5*hlast*(2*f[+,]-f[1,]-f[nrow(f),]));end;end;end;return(cp);finish;

storemodule=cprob;storemodule=drift;

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(责任编辑:郭海强)

(上接第359页)

能够发掘出更多的信息,尤其适合分析复杂生物数

[26]

据;ElonCorrea和RoystonGoodacre(2011)提出了BN),一种新型的遗传算法—贝叶斯网络方法(GA-这还能种方法在有效筛选变量并提高分类效果的同时,研究变量间的相互作用和关系

[27]

。毫无疑问,这些新

方法的提出将会为代谢组学数据分析提供新的思路和

契机。随着各种代谢组学检测仪器的快速发展,更有效的代谢组学数据分析技术亟待开发,值得更多的生物统计学者关注和研究。

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