{"title":"微阵列癌症基因表达数据分析的神经模糊集成方法","authors":"Zhenyu Wang, Vasile Palade, Yong Xu","doi":"10.1109/ISEFS.2006.251144","DOIUrl":null,"url":null,"abstract":"A neuro-fuzzy ensemble model (NFE) is proposed in this paper for analysing the gene expression data from microarray experiments. The proposed approach was tested on three benchmark cancer gene expression data sets. Experimental results show that our NFE model can be used as an efficient computational tool for microarray data analysis. In addition, compared to some most widely used approaches, neuro-fuzzy (NF)-based models not only supply good classification results, but their behavior can also be explained and interpreted in human understandable terms, which provides the researchers with a better understanding of the data","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"90","resultStr":"{\"title\":\"Neuro-Fuzzy Ensemble Approach for Microarray Cancer Gene Expression Data Analysis\",\"authors\":\"Zhenyu Wang, Vasile Palade, Yong Xu\",\"doi\":\"10.1109/ISEFS.2006.251144\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A neuro-fuzzy ensemble model (NFE) is proposed in this paper for analysing the gene expression data from microarray experiments. The proposed approach was tested on three benchmark cancer gene expression data sets. Experimental results show that our NFE model can be used as an efficient computational tool for microarray data analysis. In addition, compared to some most widely used approaches, neuro-fuzzy (NF)-based models not only supply good classification results, but their behavior can also be explained and interpreted in human understandable terms, which provides the researchers with a better understanding of the data\",\"PeriodicalId\":269492,\"journal\":{\"name\":\"2006 International Symposium on Evolving Fuzzy Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"90\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2006 International Symposium on Evolving Fuzzy Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISEFS.2006.251144\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2006 International Symposium on Evolving Fuzzy Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISEFS.2006.251144","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neuro-Fuzzy Ensemble Approach for Microarray Cancer Gene Expression Data Analysis
A neuro-fuzzy ensemble model (NFE) is proposed in this paper for analysing the gene expression data from microarray experiments. The proposed approach was tested on three benchmark cancer gene expression data sets. Experimental results show that our NFE model can be used as an efficient computational tool for microarray data analysis. In addition, compared to some most widely used approaches, neuro-fuzzy (NF)-based models not only supply good classification results, but their behavior can also be explained and interpreted in human understandable terms, which provides the researchers with a better understanding of the data