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• W3148415067 abstract "One challenge in miRNA–genes–diseases interaction studies is that it is challenging to find labeled data that indicate a positive or negative relationship between miRNA and genes. The use of one-class classification methods shows a promising path for validating them. We have applied two one-class classification methods, Isolation Forest and One-class SVM, to validate miRNAs interactions with the ERBB2 gene present in breast cancer scenarios using features extracted via sequence-binding. We found that the One-class SVM outperforms the Isolation Forest model, with values of sensitivity of 80.49% and a specificity of 86.49% showing results that are comparable to previous studies. Additionally, we have demonstrated that the use of features extracted from a sequence-based approach (considering miRNA and gene sequence binding characteristics) and one-class models have proven to be a feasible method for validating these genetic molecule interactions." @default.
• W3148415067 created "2021-04-13" @default.
• W3148415067 creator A5007249668 @default.
• W3148415067 creator A5068455777 @default.
• W3148415067 date "2021-12-01" @default.
• W3148415067 modified "2023-10-17" @default.
• W3148415067 title "One-class models for validation of miRNAs and ERBB2 gene interactions based on sequence features for breast cancer scenarios" @default.
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• W3148415067 doi "https://doi.org/10.1016/j.icte.2021.03.001" @default.
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