Abstract
BackgroundThe abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification.ResultsExperimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection methods develops a robust model with higher accuracy than the conventional microarray classification models such as support vector machine, artificial neural network, decision trees, k nearest neighbors, and diagonal linear discriminant analysis. Furthermore, the interpretable rule-base inferred from fuzzy support vector machine helps extracting biological knowledge from microarray data.ConclusionsFuzzy support vector machine as a new classification model with high generalization power, robustness, and good interpretability seems to be a promising tool for gene expression microarray classification.
Highlights
The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems
It contains the expression levels of 6817 gene probes of 72 patients, among which, 47 patients suffer from the acute lymphoblastic leukemia (ALL) and 25 patients suffer from the acute myeloid leukemia (AML)
As you see in this table the performance of fuzzy support vector machine classifier is better than SVM, artificial neural network (ANN), k nearest neighbors (KNN), classification and regression trees (CART), and diagonal linear discriminant analysis (DLDA) classifiers when we use a feature selection method as simple as SNR before setting up the classification model
Summary
The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. These algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. Transcription of the genetic information contained within the DNA into messenger RNA (mRNA) molecules is called gene expression. These mRNAs are later translated into the proteins that perform most of the critical functions of cells.
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