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- W2964792397 abstract "Among the large number of known microRNAs (miRNAs), some miRNAs play negligible roles in cell regulation. Therefore, selecting essential miRNAs is an important initial step for a deeper understanding of miRNAs and their functions. In this study, we generated 60 classification models by combining 12 representative feature extraction methods and 5 commonly used classification algorithms. The optimal model for essential miRNA classification that we obtained is based on the Mismatch feature extraction method combined with the random forest algorithm. The F-Measure, area under the curve, and accuracy values of this model were 93.2%, 96.7%, and 93.0%, respectively. We also found that the distribution of the positive and negative examples of the first few features greatly influenced the classification results. The feature extraction methods performed best when the differences between the positive and negative examples were obvious, and this led to better classification of essential miRNAs. Because each classifier's predictions for the same sample may be different, we employed a novel voting method to improve the accuracy of the classification of essential miRNAs. The performance results showed that the best classification results were obtained when five classification models were used in the voting. The five classification models were constructed based on the Mismatch, pseudo-distance structure status pair composition, Subsequence, Kmer, and Triplet feature extraction methods. The voting result was 95.3%. Our results suggest that the voting method can be an important tool for selecting essential miRNAs." @default.
- W2964792397 created "2019-08-13" @default.
- W2964792397 creator A5017426085 @default.
- W2964792397 creator A5024982875 @default.
- W2964792397 creator A5054850777 @default.
- W2964792397 creator A5060174737 @default.
- W2964792397 date "2019-12-01" @default.
- W2964792397 modified "2023-10-14" @default.
- W2964792397 title "Selecting Essential MicroRNAs Using a Novel Voting Method" @default.
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- W2964792397 doi "https://doi.org/10.1016/j.omtn.2019.07.019" @default.
- W2964792397 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6727015" @default.
- W2964792397 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31479921" @default.
- W2964792397 hasPublicationYear "2019" @default.
- W2964792397 type Work @default.