代谢组学数据分析的统计学方法(4)
发布时间:2021-06-06
发布时间:2021-06-06
ChineseJournalofHealthStatistics,Apr2014,Vol.31,No.2·365·
quit;
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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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(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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