本文關(guān)鍵詞：基于統(tǒng)計相關(guān)性與K-means的區(qū)分基因子集選擇算法，由筆耕文化傳播整理發(fā)布。

基于統(tǒng)計相關(guān)性與K-means的區(qū)分基因子集選擇算法

Statistical Correlation and K-Means Based Distinguishable Gene Subset Selection Algorithms

[1] [2]

XIE Juan-Ying, GAO Hong-Chao （School of Computer Science, Shaanxi Normal University, Xi＇an 710062, China）

陜西師范大學計算機科學學院,陜西西安710062

文章摘要：針對高維小樣本癌癥基因數(shù)據(jù)集的有效區(qū)分基因子集選擇難題,提出基于統(tǒng)計相關(guān)性和K-means的新穎混合基因選擇算法實現(xiàn)有效區(qū)分基因子集選擇。算法首先采用Pearson相關(guān)系數(shù)和Wilcoxon秩和檢驗計算各基因與類標的相關(guān)性,根據(jù)統(tǒng)計相關(guān)性原則選取與類標相關(guān)性較大的若干基因構(gòu)成預選擇基因子集；然后,采用K-means算法將預選擇基因子集中高度相關(guān)的基因聚集到同一類簇,訓練 SVM 分類模型,計算每一個基因的權(quán)重,從每一類簇選擇一個權(quán)重最大或者采用輪盤賭思想從每一類簇選擇一個得票數(shù)最多的基因作為本類簇的代表基因,各類簇的代表基因構(gòu)成有效區(qū)分基因子集。將該算法與采用隨機策略選擇各類簇代表基因的隨機基因選擇算法 Random, Guyon的經(jīng)典基因選擇算法SVM-RFE、采用順序前向搜索策略的基因選擇算法SVM-SFS進行實驗比較,幾個經(jīng)典基因數(shù)據(jù)集上的200次重復實驗的平均實驗結(jié)果表明：所提出的混合基因選擇算法能夠選擇到區(qū)分性能非常好的基因子集,建立在該區(qū)分基因子集上的分類器具有非常好的分類性能。

Abstr：To deal with the challenging problem of recognizing the small number of distinguishable genes which can tell the cancer patients from normal people in a dataset with a small number of samples and tens of thousands of genes, novel hybrid gene selection algorithms are proposed in this paper based on the statistical correlation and K-means algorithm. The Pearson correlation coefficient and Wilcoxon signed-rank test are respectively adopted to calculate the importance of each gene to the classification to filter the least important genes and preserve about 10 percent of the important genes as the pre-selected gene subset. Then the related genes in the pre-selected gene subset are clustered via K-means algorithm, and the weight of each gene is calculated from the related coefficient of the SVM classifier. The most important gene, with the biggest weight or with the highest votes when the roulette wheel strategy is used, is chosen as the representative gene of each cluster to construct the distinguishable gene subset. In order to verify the effectiveness of the proposed hybrid gene subset selection algorithms, the random selection strategy （named Random） is also adopted to select the representative genes from clusters. The proposed distinguishable gene subset selection algorithms are compared with Random and the very popular gene selection algorithm SVM-RFE by Guyon and the pre-studied gene selection algorithm SVM-SFS. The average experimental results of 200 runs of the aforementioned gene selection algorithms on some classic and very popular gene expression datasets with extensive experiments demonstrate that the proposed distinguishable gene subset selection algorithms can find the optimal gene subset, and the cl

文章關(guān)鍵詞：

Keyword：:distinguishable gene subset selection Pearson correlation coefficient Wilcxon singed-rank test K-means clustering statistical correlation Filter algorithms Wrapper algorithms

課題項目：國家自然科學基金（31372250）;中央高�；究蒲袠I(yè)務(wù)費專項基金（GK201102007）;陜西省科技攻關(guān)項目（2013K12-03-24）

  本文關(guān)鍵詞：基于統(tǒng)計相關(guān)性與K-means的區(qū)分基因子集選擇算法，，由筆耕文化傳播整理發(fā)布。