数据挖掘_Yeast Gene Expression Data(酵母基因表达数据)

Y east Gene Expression Data(酵母基因表达数据)

数据摘要：

These are the data from the paper Support V ector Machine Classification of Microarray Gene Expression Data.

中文关键词：

数据挖掘,生物学,DNA,酵母,杂交试验,机器学习,

英文关键词：

Data mining,Biology,DNA,Y east,Hybridization experiment,Machine Learning,

数据格式：

TEXT

数据用途：

The data can be used to data mining and analysis.

数据详细介绍：

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 the 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 classifying 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:

o2467 genes

o79 measurements, 6 class labels

o 1.8 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 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. 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 selection of these 5 functional classes for the SVM

classification task.

∙Stanford web site

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