數(shù)據(jù)挖掘_Yeast Gene Expression Data(酵母基因表達數(shù)據(jù))

1、Yeast Gene Expression Data(酵母基因表達數(shù)據(jù))數(shù)據(jù)摘要：These are the data from the paper Support Vector Machine Classification of Microarray Gene Expression Data.中文關(guān)鍵詞：數(shù)據(jù)挖掘,生物學,DNA,酵母,雜交試驗,機器學習,英文關(guān)鍵詞：Data mining,Biology,DNA,Yeast,Hybridization experiment,Machine Learning,數(shù)據(jù)格式：TEXT數(shù)據(jù)用途：The data can be used to data

2、 mining and analysis.數(shù)據(jù)詳細介紹：Yeast gene expression data · Description: These are the data from the paper Support Vector Machine Classification of Microarray Gene Expression Data. For 2467 genes, gene expression levels were measured in 79 different situations (here is the raw data set). Some of t

3、he measurements follow each other up in time, but in the paper they were not treated as time series (although to a certain extend that would be possible). For each of these genes, it is given whether they belong to one of 6 functional classes (class lables on-line). The paper is concerned with class

4、ifying genes in into 5 of these classes (one class is unpredictable). The data contain many genes that belong to other functional classes than these 5, but those are not discernable on the basis of their gene expression levels alone. · Size: o 2467 genes o 79 measurements, 6 class labels o 1.8

5、MB: 1.7 MB measurement data and 125 KB labels · References: o Support Vector Machine Classification of Microarray Gene Expression Data (1999) by M. P. S. Brown, W. N. Grundy, D. Lin, N. Cristianini, C. Sugnet, T. S. Furey, M. Ares Jr. and D. Hausslerhref (local copy): This is the original paper

6、 from which the data were obtained. It uses SVM's to classify the genes, and compares this to other methods like decision trees. A good description of difficulties with the data can also be found here. o Cluster analysis and display of genome-wide expression patterns (1998) by M. B. Eisen, P. T.

7、 Spellman, P. O. Brown and D. Botstein: This paper describes clustering of genes. The results of this paper showed that the 5 different classes Brown et Al. are trying to predict more or less cluster together. So it indicated that these classes were discernable based on the gene expression levels. This was the basis for the