This article is part of the supplement: The 2010 International Conference on Bioinformatics and Computational Biology (BIOCOMP 2010): Genomics
Research article
Gene selection and classification for cancer microarray data based on machine learning and similarity measures
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* Corresponding authors: Andrew H Sung sung@cs.nmt.edu - Youping Deng Youping_Deng@rush.edu
1 Department of Computer Science, Sam Houston State University, Huntsville, TX 77341, USA
2 Department of Computer Science and Institute of Complex Additive Systems Analysis, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA
3 Biostatistics Epidemiology Research Design Core, Center for Clinical and Translational Sciences, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
4 The Chem21 Group, Inc, 1780 Wilson Drive, Lake Forest, IL 60045, USA
5 Mathematics and Computer Science, Dept. of Mathematics & Computer Science, South Dakota School of Mines & Technology, Rapid City, SD 57701-3995
6 Wuhan University of Science and Technology, Wuhan, Hubei 430081, China
7 Conjugate and Medicinal Chemistry Laboratory, Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
8 Cancer Bioinformatics, Rush University Cancer Center, and Department of Internal Medicine, Rush University Medical Center, Chicago, IL 60612, USA
BMC Genomics 2011, 12(Suppl 5):S1 doi:10.1186/1471-2164-12-S5-S1
Published: 23 December 2011Abstract
Background
Microarray data have a high dimension of variables and a small sample size. In microarray data analyses, two important issues are how to choose genes, which provide reliable and good prediction for disease status, and how to determine the final gene set that is best for classification. Associations among genetic markers mean one can exploit information redundancy to potentially reduce classification cost in terms of time and money.
Results
To deal with redundant information and improve classification, we propose a gene selection method, Recursive Feature Addition, which combines supervised learning and statistical similarity measures. To determine the final optimal gene set for prediction and classification, we propose an algorithm, Lagging Prediction Peephole Optimization. By using six benchmark microarray gene expression data sets, we compared Recursive Feature Addition with recently developed gene selection methods: Support Vector Machine Recursive Feature Elimination, Leave-One-Out Calculation Sequential Forward Selection and several others.
Conclusions
On average, with the use of popular learning machines including Nearest Mean Scaled Classifier, Support Vector Machine, Naive Bayes Classifier and Random Forest, Recursive Feature Addition outperformed other methods. Our studies also showed that Lagging Prediction Peephole Optimization is superior to random strategy; Recursive Feature Addition with Lagging Prediction Peephole Optimization obtained better testing accuracies than the gene selection method varSelRF.