One-class models for validation of miRNAs and ERBB2 gene interactions based on sequence features for breast cancer scenarios

    Research output: Contribution to journalArticle (Contribution to Journal)peer-review

    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.

    Original languageEnglish
    JournalICT Express
    DOIs
    StateAccepted/In press - 2021

    Keywords

    • Breast cancer
    • MiRNAs
    • One-class models
    • Unsupervised learning

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