基于概率模型-gMOS的基因芯片数据分析  

Analysis of Probe-level Gene Expression Based on Probablistic Model gMOS

作　　者：金圣华[1] 刘红[1] JIN Sheng-hua,LIU Hong（Department of Compute Science and Technology,Huaiyin Institute of Technology,Huaian 223003,China）

机构地区：[1]淮阴工学院计算机工程学院,江苏淮安223003

出　　处：《电脑知识与技术》2011年第9期6212-6213,共2页Computer Knowledge and Technology

摘　　要：基因芯片技术近年来广泛用于农业和林业中,但是芯片数据中大量噪声的存在影响了研究者对农植物生长和发育的根本机理的认识。文章采用gMOS概率模型来分析基因芯片原始数据,通过spike-in数据集实验论证,结果表明可以利用概率模型gMOS来计算基因表达值,同时还能获得基因表达值的置信区间,在一定程度上降低了非特异性杂交引起的噪音影响。Affymetrix microarray are currentry the most widely used in the agriculture and forestry,However,the existence of a lot of noise in the microarray datas affects the basic understanding of researchers on plant growth and development The thesis studies the probe-level data through the probabilistic model gMOS,Experiment results on a standard spike-in dataset shows that the probabilistic model not only measures the gene expression level but also provides a level of uncertainty of the measurement,the influence of the noise caused by cross-hybridization is decreased to some degree.

关 键 词：基因芯片 GMOS 概率模型 

分 类 号：TP18[自动化与计算机技术—控制科学与工程;自动化与计算机技术—控制理论与控制工程]