An improved fuzzy set-based multifactor dimensionality reduction for detecting epistasis.
Artif Intell Med
; 102: 101768, 2020 01.
Article
em En
| MEDLINE
| ID: mdl-31980105
ABSTRACT
OBJECTIVE:
Epistasis identification is critical for determining susceptibility to human genetic diseases. The rapid development of technology has enabled scalability to make multifactor dimensionality reduction (MDR) measurements an effective calculation tool that achieves superior detection. However, the classification of high-risk (H) or low-risk (L) groups in multidrug resistance operations calls for extensive research. METHODS ANDMATERIAL:
In this study, an improved fuzzy sigmoid (FS) method using the membership degree in MDR (FSMDR) was proposed for solving the limitations of binary classification. The FS method combined with MDR measurements yielded an improved ability to distinguish similar frequencies of potential multifactor genotypes.RESULTS:
We compared our results with other MDR-based methods and FSMDR achieved superior detection rates on simulated data sets. The results indicated that the fuzzy classifications can provide insight into the uncertainty of H/L classification in MDR operation.CONCLUSION:
FSMDR successfully detected significant epistasis of coronary artery disease in the Wellcome Trust Case Control Consortium data set.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Lógica Fuzzy
/
Epistasia Genética
/
Redução Dimensional com Múltiplos Fatores
Tipo de estudo:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Artif Intell Med
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2020
Tipo de documento:
Article