Gene Selection for Microarray-based Cancer Classification Using Genetic Algorithm 이 정문 2003/04/01 BI Lab.

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Presentation transcript:

Gene Selection for Microarray-based Cancer Classification Using Genetic Algorithm 이 정문 2003/04/01 BI Lab

Introduction Microarray can be used for cancer classification based on gene expression. Selection of informative genes for sample discrimination can improve the cancer classification. I use the genetic algorithm (GA) and k-nearest neighbor to find informative genes in multi-class microarray cancer data .

Gene Expression Data sample1 sample2 sample3 sample4 sample5 … 1 0.46 0.30 0.80 1.51 0.90 ... 2 -0.10 0.49 0.24 0.06 0.46 ... 3 0.15 0.74 0.04 0.10 0.20 ... 4 -0.45 -1.03 -0.79 -0.56 -0.32 ... 5 -0.06 1.06 1.35 1.09 -1.09 ... Genes 3 Gene expression level of gene i in mRNA sample j Tens or hundreds of samples Vs. Thousands of genes => Need to select informative genes

Rank-based selection methods For each gene, Signal-to-noise = (1 - 2) / ( 1 +  2) BSS/WSS = Are good at identifying genes which are strongly correlated with the target phenotype class distinction but ignore the interaction between genes

GA/kNN method(Leping Li,2001) Initial chromosomes consisting of d genes (In this case d = 5) G1 G35 G7 G21 G3 G32 G5 G1 G21 G10 G6 G3 For each chromosome, assign fitness (the number of samples correctly classified by kNN) G1 G35 G7 G21 G3 G32 G5 G23 G10 G6 Replacement Selection G1 G21 G10 G6 G3 Mutation G1 G23 G10 G6 G3 Is termination criterion met? no yes Save the chromosome

Datasets GCM NCI60 Ramaswamy et al, 2001 14 classes 190 samples (144 training set + 46 test set) 16,063 genes NCI60 Ross et al, 2000 9 classes 60 cancer cell lines 9,703 genes

Issues Choice of termination criterion Computationally intensive One-Vs-All classification : build n classifier for each n class Whether to use crossover Lamarckian GA (?)