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A New Algorithm for Analysis of MiRNA Expression Profiles—SVM-RFE-FKNN

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Based on MicroRNA (miRNA) expression profiles, this article proposes a new algorithm—SVM-RFE-FKNN, which combines the support vector machine-recursive feature elimination (SVM-RFE) algorithm and the fuzzy K -nearest neighbor (FKNN) algorithm, to realize binary classification of tumors. First, the SVM-RFE algorithm was used to select features from the miRNA expression profile dataset to constitute feature subsets and to determine the maximum number of support vectors. Next, this maximum number was regarded as the upper limit of the parameter K in the FKNN algorithm that was then used to classify the samples to be tested. Finally, the leave-one-out cross-validation method was adopted to assess the classification performance of the proposed algorithm. Through experiments, our proposed algorithm was compared with other twelve classification methods, and the result shows that our algorithm had better classification performance. Specifically, with only a few miRNA biomarkers, the proposed algorithm could reach an accuracy of 99.46% and an area under the receiver operating characteristic curve (AUC) of 0.9874.
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Affiliations: 1: College of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang 524088, China 2: College of Mechanical and Power Engineering, Guangdong Ocean University, Zhanjiang 524088, China 3: Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), Zhanjiang 524088, China 4: Guangdong Marine Equipment and Manufacturing Engineering Research Center, Zhanjiang 524088, China

Appeared or available online: January 29, 2021

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