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

• Autores: Gutiérrez Cárdenas, Juan Manuel, Wan, Zenghui
• Formato: artículo
• Fecha de Publicación: 2021
• Institución: Universidad de Lima
• Repositorio: ULIMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ulima.edu.pe:20.500.12724/12936
• Enlace del recurso: https://hdl.handle.net/20.500.12724/12936; https://doi.org/10.1016/j.icte.2021.03.001
• Nivel de acceso: acceso abierto
• Materia: Breast cancer; Molecular genetics; Cáncer de mama; Genética molecular; https://purl.org/pe-repo/ocde/ford#2.02.04

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.