【論】Chilingaryan,2002,Multivariate approach for ～

A.Chilingaryan, N.Gevorgyan, A.Vardanyan, D.Jones and A.Szabo
Multivariate approach for selecting sets of differentially expressed genes
Mathematical Biosciences Volume 176, Issue 1, March 2002, Pages 59-69
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・遺伝子抽出のための多変量解析的方法（multi-start random search method with early stopping (MRSES)）の提案。マハラノビス距離を指標にして遺伝子を抽出する。
・データ
1.人工データ
2.Two colon cancer cell lines (HT29, HCT116)

・問題点「Currently used approaches ignore the multidimensional structure of the data. However it is well known that correlation among covariates can enhance the ability to detect less pronounced differences.」
・方法「We use the Mahalanobis distance between vectors of gene expressions as a criterion for simultaneously comparing a set of genes and develop an algorithm for maximizing it.」
・「It is well known that genes do not work independently; activation of one gene usually triggers changes in the expression level of other genes, that is genes are involved in so-called pathways.」
・特徴「In this paper we develop an approach that uses both the mean expression levels and the covariance structure of the data.」
・問題点「First, the number of possible gene combinations is enormously large; therefore it is impossible to compare all gene subsets and find the optimal one. On the other hand, if a global optimum could be found, it would be overly training sample specific, because of the phenomenon of overfitting.」
・問題点「Unfortunately, the problem of developing a simulation model for microarray data has been largely ignored, we are aware of only one attempt [19] in which a highly specific parametric model assuming independent genes is used.」