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

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 cl...

Description complète

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Juan Gutiérrez-Cárdenas, Zenghui Wang
Format:	article
Langue:	EN
Publié:	Elsevier 2021
Sujets:	MiRNAs Breast cancer One-class models Unsupervised learning Information technology T58.5-58.64
Accès en ligne:	https://doaj.org/article/e204d2fd76f0478291c94729ba0c2bfb
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

id	oai:doaj.org-article:e204d2fd76f0478291c94729ba0c2bfb
record_format	dspace
spelling	oai:doaj.org-article:e204d2fd76f0478291c94729ba0c2bfb2021-11-30T04:16:36ZOne-class models for validation of miRNAs and ERBB2 gene interactions based on sequence features for breast cancer scenarios2405-959510.1016/j.icte.2021.03.001https://doaj.org/article/e204d2fd76f0478291c94729ba0c2bfb2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2405959521000333https://doaj.org/toc/2405-9595One 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.Juan Gutiérrez-CárdenasZenghui WangElsevierarticleMiRNAsBreast cancerOne-class modelsUnsupervised learningInformation technologyT58.5-58.64ENICT Express, Vol 7, Iss 4, Pp 468-474 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	MiRNAs Breast cancer One-class models Unsupervised learning Information technology T58.5-58.64
spellingShingle	MiRNAs Breast cancer One-class models Unsupervised learning Information technology T58.5-58.64 Juan Gutiérrez-Cárdenas Zenghui Wang One-class models for validation of miRNAs and ERBB2 gene interactions based on sequence features for breast cancer scenarios
description	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.
format	article
author	Juan Gutiérrez-Cárdenas Zenghui Wang
author_facet	Juan Gutiérrez-Cárdenas Zenghui Wang
author_sort	Juan Gutiérrez-Cárdenas
title	One-class models for validation of miRNAs and ERBB2 gene interactions based on sequence features for breast cancer scenarios
title_short	One-class models for validation of miRNAs and ERBB2 gene interactions based on sequence features for breast cancer scenarios
title_full	One-class models for validation of miRNAs and ERBB2 gene interactions based on sequence features for breast cancer scenarios
title_fullStr	One-class models for validation of miRNAs and ERBB2 gene interactions based on sequence features for breast cancer scenarios
title_full_unstemmed	One-class models for validation of miRNAs and ERBB2 gene interactions based on sequence features for breast cancer scenarios
title_sort	one-class models for validation of mirnas and erbb2 gene interactions based on sequence features for breast cancer scenarios
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/e204d2fd76f0478291c94729ba0c2bfb
work_keys_str_mv	AT juangutierrezcardenas oneclassmodelsforvalidationofmirnasanderbb2geneinteractionsbasedonsequencefeaturesforbreastcancerscenarios AT zenghuiwang oneclassmodelsforvalidationofmirnasanderbb2geneinteractionsbasedonsequencefeaturesforbreastcancerscenarios
_version_	1718406836186513408

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

Documents similaires