计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2010年
9期
33-37
,共5页
基因芯片数据%共调控基因%Pearson相关系数%闭合频繁模式
基因芯片數據%共調控基因%Pearson相關繫數%閉閤頻繁模式
기인심편수거%공조공기인%Pearson상관계수%폐합빈번모식
microarray data%co-regulated genes%Pearson correlation coefficient%closed frequent pattern
针对基因芯片数据高噪音、列(基因)数比行(实验条件)数多几个数量级的特殊性,为了进一步提高从基因芯片数据挖掘共调控基因的时间效率和挖掘结果的有效性,首先根据所有两两基因对之间的Pearson相关系数对原始完整数据集进行分组,然后使用列(基因)枚举方法对各组数据分别进行闭合频繁模式挖掘,并对活化和抑制共调控关系的挖掘分别进行处理.实验结果证明:算法快速有效地挖掘出了两种共调控基因.
針對基因芯片數據高譟音、列(基因)數比行(實驗條件)數多幾箇數量級的特殊性,為瞭進一步提高從基因芯片數據挖掘共調控基因的時間效率和挖掘結果的有效性,首先根據所有兩兩基因對之間的Pearson相關繫數對原始完整數據集進行分組,然後使用列(基因)枚舉方法對各組數據分彆進行閉閤頻繁模式挖掘,併對活化和抑製共調控關繫的挖掘分彆進行處理.實驗結果證明:算法快速有效地挖掘齣瞭兩種共調控基因.
침대기인심편수거고조음、렬(기인)수비행(실험조건)수다궤개수량급적특수성,위료진일보제고종기인심편수거알굴공조공기인적시간효솔화알굴결과적유효성,수선근거소유량량기인대지간적Pearson상관계수대원시완정수거집진행분조,연후사용렬(기인)매거방법대각조수거분별진행폐합빈번모식알굴,병대활화화억제공조공관계적알굴분별진행처리.실험결과증명:산법쾌속유효지알굴출료량충공조공기인.
Microarray data sets typically contain strong noise and an order of magnitude more genes than experiments.To further reduce the running time and improve the validity of co-regulated genes mined from microarray data,a new method is proposed which firstly groups all genes according to the Pearson correlation coefficient between every two genes,then uses column(gene) enumeration to mine closed frequent patterns as positive or negative co-regulated genes for each group.The experimental results show that the proposed approach can quickly and effectively mine two kinds of co-regulated genes from microarray data.
