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
Article number4
Pages (from-to)468-474
Number of pages7
JournalICT Express
Volume7
Issue number4
DOIs
StatePublished - 1 Mar 2021

Keywords

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

COAR

  • Article

OECD Category

  • Ingeniería de sistemas y comunicaciones

Ulima Repository Category

  • Ciencias / Medicina y Salud

Ulima Repository Subject

  • Breast cancer
  • Cáncer de mama
  • Genética molecular
  • Molecular genetics

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