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A comparative study of different machine learning methods on microarray gene expression data

Overview of attention for article published in BMC Genomics, March 2008
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Title
A comparative study of different machine learning methods on microarray gene expression data
Published in
BMC Genomics, March 2008
DOI 10.1186/1471-2164-9-s1-s13
Pubmed ID
Authors

Mehdi Pirooznia, Jack Y Yang, Mary Qu Yang, Youping Deng

Abstract

Several classification and feature selection methods have been studied for the identification of differentially expressed genes in microarray data. Classification methods such as SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods have been used in recent studies. The accuracy of these methods has been calculated with validation methods such as v-fold validation. However there is lack of comparison between these methods to find a better framework for classification, clustering and analysis of microarray gene expression results. In this study, we compared the efficiency of the classification methods including; SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods. The v-fold cross validation was used to calculate the accuracy of the classifiers. Some of the common clustering methods including K-means, DBC, and EM clustering were applied to the datasets and the efficiency of these methods have been analysed. Further the efficiency of the feature selection methods including support vector machine recursive feature elimination (SVM-RFE), Chi Squared, and CSF were compared. In each case these methods were applied to eight different binary (two class) microarray datasets. We evaluated the class prediction efficiency of each gene list in training and test cross-validation using supervised classifiers. We presented a study in which we compared some of the common used classification, clustering, and feature selection methods. We applied these methods to eight publicly available datasets, and compared how these methods performed in class prediction of test datasets. We reported that the choice of feature selection methods, the number of genes in the gene list, the number of cases (samples) substantially influence classification success. Based on features chosen by these methods, error rates and accuracy of several classification algorithms were obtained. Results revealed the importance of feature selection in accurately classifying new samples and how an integrated feature selection and classification algorithm is performing and is capable of identifying significant genes.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 306 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 <1%
United Kingdom 2 <1%
France 1 <1%
Austria 1 <1%
Chile 1 <1%
India 1 <1%
Ukraine 1 <1%
Brazil 1 <1%
Spain 1 <1%
Other 3 <1%
Unknown 291 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 79 26%
Researcher 55 18%
Student > Master 33 11%
Student > Bachelor 25 8%
Professor > Associate Professor 16 5%
Other 50 16%
Unknown 48 16%
Readers by discipline Count As %
Computer Science 83 27%
Agricultural and Biological Sciences 63 21%
Biochemistry, Genetics and Molecular Biology 38 12%
Engineering 20 7%
Medicine and Dentistry 12 4%
Other 30 10%
Unknown 60 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 November 2017.
All research outputs
#7,531,132
of 22,979,862 outputs
Outputs from BMC Genomics
#3,629
of 10,687 outputs
Outputs of similar age
#28,784
of 82,115 outputs
Outputs of similar age from BMC Genomics
#11
of 24 outputs
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So far Altmetric has tracked 10,687 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 59% of its peers.
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