{"title":"集合布谷鸟搜索双聚类的基因表达数据","authors":"Lu Yin, Yongguo Liu","doi":"10.1109/ICCI-CC.2016.7862071","DOIUrl":null,"url":null,"abstract":"Many biclustering algorithms have been proposed in analyzing the gene expression data and ensemble biclustering methods can improve performance of the biclustering algorithm. We propose a new method of obtaining a variety of constituent biclusters which use different quality measures of bicluster. To demonstrate the efficiency of our methods, experiment on six real gene expression data shows the diversity and biological significance of the biclusters obtained by our methods are higher than that of the compared methods.","PeriodicalId":135701,"journal":{"name":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Ensemble cuckoo search biclustering of the gene expression data\",\"authors\":\"Lu Yin, Yongguo Liu\",\"doi\":\"10.1109/ICCI-CC.2016.7862071\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many biclustering algorithms have been proposed in analyzing the gene expression data and ensemble biclustering methods can improve performance of the biclustering algorithm. We propose a new method of obtaining a variety of constituent biclusters which use different quality measures of bicluster. To demonstrate the efficiency of our methods, experiment on six real gene expression data shows the diversity and biological significance of the biclusters obtained by our methods are higher than that of the compared methods.\",\"PeriodicalId\":135701,\"journal\":{\"name\":\"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCI-CC.2016.7862071\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCI-CC.2016.7862071","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Ensemble cuckoo search biclustering of the gene expression data
Many biclustering algorithms have been proposed in analyzing the gene expression data and ensemble biclustering methods can improve performance of the biclustering algorithm. We propose a new method of obtaining a variety of constituent biclusters which use different quality measures of bicluster. To demonstrate the efficiency of our methods, experiment on six real gene expression data shows the diversity and biological significance of the biclusters obtained by our methods are higher than that of the compared methods.