TY - JOUR
T1 - One-class models for validation of miRNAs and ERBB2 gene interactions based on sequence features for breast cancer scenarios
AU - Gutiérrez-Cárdenas, Juan
AU - Wang, Zenghui
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2021/3/1
Y1 - 2021/3/1
N2 - 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.
AB - 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.
KW - Breast cancer
KW - MiRNAs
KW - One-class models
KW - Unsupervised learning
UR - https://hdl.handle.net/20.500.12724/12936
UR - http://www.scopus.com/inward/record.url?scp=85104331563&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/8c844bc5-cd72-3152-aac5-22af7b03118f/
U2 - 10.1016/j.icte.2021.03.001
DO - 10.1016/j.icte.2021.03.001
M3 - Artículo (Contribución a Revista)
AN - SCOPUS:85104331563
SN - 2405-9595
VL - 7
SP - 468
EP - 474
JO - ICT Express
JF - ICT Express
IS - 4
M1 - 4
ER -
