{"title":"跨平台微阵列数据集成的归一化线性变换","authors":"Huilin Xiong, Ya Zhang, Xue-wen Chen","doi":"10.1109/ICMLA.2007.65","DOIUrl":null,"url":null,"abstract":"With microarray data being dramatically accumulated, integrating data from related studies represents a natural way to increase sample size so that more reliable statistical analysis may be performed. However, inherent variation among different microarray platforms makes the data integration not a trivial task. In this paper, we present a simple and effective integration scheme, called normalized linear transform (NLT), to combine data from different microarray platforms. The NLT scheme is compared with three other integration schemes for two tasks: classification analysis and gene marker selection. Our experiments demonstrate that the NLT scheme performs best in terms of classification accuracy under various classification settings, and leads to more biologically significant marker genes.","PeriodicalId":448863,"journal":{"name":"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Normalized Linear Transform for Cross-Platform Microarray Data Integration\",\"authors\":\"Huilin Xiong, Ya Zhang, Xue-wen Chen\",\"doi\":\"10.1109/ICMLA.2007.65\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"With microarray data being dramatically accumulated, integrating data from related studies represents a natural way to increase sample size so that more reliable statistical analysis may be performed. However, inherent variation among different microarray platforms makes the data integration not a trivial task. In this paper, we present a simple and effective integration scheme, called normalized linear transform (NLT), to combine data from different microarray platforms. The NLT scheme is compared with three other integration schemes for two tasks: classification analysis and gene marker selection. Our experiments demonstrate that the NLT scheme performs best in terms of classification accuracy under various classification settings, and leads to more biologically significant marker genes.\",\"PeriodicalId\":448863,\"journal\":{\"name\":\"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)\",\"volume\":\"105 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-12-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLA.2007.65\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2007.65","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Normalized Linear Transform for Cross-Platform Microarray Data Integration
With microarray data being dramatically accumulated, integrating data from related studies represents a natural way to increase sample size so that more reliable statistical analysis may be performed. However, inherent variation among different microarray platforms makes the data integration not a trivial task. In this paper, we present a simple and effective integration scheme, called normalized linear transform (NLT), to combine data from different microarray platforms. The NLT scheme is compared with three other integration schemes for two tasks: classification analysis and gene marker selection. Our experiments demonstrate that the NLT scheme performs best in terms of classification accuracy under various classification settings, and leads to more biologically significant marker genes.