Thanyaluk Jirapech-Umpai and Stuart Aitken
Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes
BMC Bioinformatics 2005, 6:148
[PDF][Web Site]
・複数(3以上)クラス識別法 "Evolutionary methods" の紹介。
・データ:1.Leukemia [Golub]; 2.NCI60 [Ross]
・比較したランキング法（ソフト：RankGene）：R1.Information gain; R2.Twoing rule; R3.Gini index; R4.Sum minority; R5.Max minority; R6.Sum of variances.
・遺伝子の評価指標：Z-score
・識別率の評価法：LOOCV; .632 bootstrap
・識別法の比較対象：GA+KNN classifier
・目的「The aim of this study is to evaluate an evolutionary algorithm for multiclass classification accuracy on microarray samples.」
・概要「The contributions of this paper are: a comprehensive evaluation of an evolutionary classifier; an investigation of feature selection in learning classifiers; an analysis of frequently selected genes, and a comparison of gene rankings across several previous studies of the leukemia data.」
・結果「Table 1 indicates that population size may be a more important factor than feature size for the baseline system.」
・「Z-score analysis is one means to determine the significance of the observed frequency of an event against that which might have occurred by chane.」
・「This indicates that the classes can be distinguished by any of a large set genes that are indicative of a category, but that these genes are not necessarily informative in the sense that they are activated in a comparable way across both the training and the testing sets.」
・結果「This study confirms that significantly different sets of genes are found to be most discriminatory as the sample classes are refined.」
・「Golub et al.[1] have normalised the dataset by re-scaling intensity values to make the overall itensities for each chip equivalent and also fitted the data with a linear regression model.」
・一番大事な "Evolutionary methods (Evolutionary algorithm)" の何たるか（特色）、がよくわからず。広く一般に知られた方法？ GAの親戚かなにか？
・あ。wikiに載ってた。。。（恥） なんとも曖昧な言葉。"Evolutionary algorithm"の考え方を取り入れたオリジナルの方法、という理解か。
Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes
BMC Bioinformatics 2005, 6:148
[PDF][Web Site]
・複数(3以上)クラス識別法 "Evolutionary methods" の紹介。
・データ:1.Leukemia [Golub]; 2.NCI60 [Ross]
・比較したランキング法（ソフト：RankGene）：R1.Information gain; R2.Twoing rule; R3.Gini index; R4.Sum minority; R5.Max minority; R6.Sum of variances.
・遺伝子の評価指標：Z-score
・識別率の評価法：LOOCV; .632 bootstrap
・識別法の比較対象：GA+KNN classifier
・目的「The aim of this study is to evaluate an evolutionary algorithm for multiclass classification accuracy on microarray samples.」
・概要「The contributions of this paper are: a comprehensive evaluation of an evolutionary classifier; an investigation of feature selection in learning classifiers; an analysis of frequently selected genes, and a comparison of gene rankings across several previous studies of the leukemia data.」
・結果「Table 1 indicates that population size may be a more important factor than feature size for the baseline system.」
・「Z-score analysis is one means to determine the significance of the observed frequency of an event against that which might have occurred by chane.」
・「This indicates that the classes can be distinguished by any of a large set genes that are indicative of a category, but that these genes are not necessarily informative in the sense that they are activated in a comparable way across both the training and the testing sets.」
・結果「This study confirms that significantly different sets of genes are found to be most discriminatory as the sample classes are refined.」
・「Golub et al.[1] have normalised the dataset by re-scaling intensity values to make the overall itensities for each chip equivalent and also fitted the data with a linear regression model.」
・一番大事な "Evolutionary methods (Evolutionary algorithm)" の何たるか（特色）、がよくわからず。広く一般に知られた方法？ GAの親戚かなにか？
・あ。wikiに載ってた。。。（恥） なんとも曖昧な言葉。"Evolutionary algorithm"の考え方を取り入れたオリジナルの方法、という理解か。
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