Pavlos Pavlidis and Panayiota Poirazi
Individualized markers optimize class prediction of microarray data
BMC Bioinformatics 2006, 7:345doi
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・クラス分けの指標となる遺伝子抽出法の提案。
・データ
(Data Set),(Selection/Classification)
1.AML/ALL, S2N(Signal to Noise)/NA(Neighborhood Analysis)
2.Breast Cancer, CC(Correlation Coefficient)/FA(Factor Analysis)
3.Lung Cancer, 2-tail(2-Tail Student Test),ER(Expression Ratio)
4.AML/MLL/ALL, CC/K-NN(K-Nearest Neighbors)
5.CNS, S2N/K-NN
6.Lymph Node, CC/FA
・問題点「
Despite the evident dissimilarity in various characteristics of biological samples belonging to the same category, most of the marker ? selection and classification methods do not consider this variability.」
・「
Among these, filter methods in which the selection is independent from the optimization criteria of the classifier are most frequently used. Such methods have the advantage of being cost-effective and easy to implement which make them very attractive for microarray data experiments where the set of features is in the order of thousands.」
・「
In fact, a recent publication [29] showed that a yeast gene expression dataset is better modeled by an alpha distribution (a = 1.3).」
・特長「
Unlike existing filter feature selection techniques, this method applies no restrictions to the mean expression values of informative genes between the different classes.」
・CERsとは「
CERs (Consistent Expression Regions) are defined as the intervals enclosing the expression (sorted in ascending order) of a given gene in a significant number of training samples which belong to the same category.」
・たいして難しい処理をしているわけでもなさそうなのに、その方法がさっぱり理解できない。
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