Classification of Common Relationships Based on Short Tandem Repeat Profiles Using Data Mining / 대한법의학회지
Korean Journal of Legal Medicine
;
: 97-105, 2019.
Artículo
en Coreano
| WPRIM
| ID: wpr-759870
ABSTRACT
We reviewed past studies on the identification of familial relationships using 22 short tandem repeat markers. As a result, we can obtain a high discrimination power and a relatively accurate cut-off value in parent-child and full sibling relationships. However, in the case of pairs of uncle-nephew or cousin, we found a limit of low discrimination power of the likelihood ratio (LR) method. Therefore, we compare the LR ranking method and data mining techniques (e.g., logistic regression, linear discriminant analysis, diagonal linear discriminant analysis, diagonal quadratic discriminant analysis, K-nearest neighbor, classification and regression trees, support vector machines, random forest [RF], and penalized multivariate analysis) that can be applied to identify familial relationships, and provide a guideline for choosing the most appropriate model under a given situation. RF, one of the data mining techniques, was found to be more accurate than other methods. The accuracy of RF is 99.99% for parent-child, 99.44% for full siblings, 90.34% for uncle-nephew, and 79.69% for first cousins.
Texto completo:
Disponible
Índice:
WPRIM (Pacífico Occidental)
Asunto principal:
Árboles
/
Modelos Logísticos
/
Bosques
/
Clasificación
/
Repeticiones de Microsatélite
/
Hermanos
/
Discriminación en Psicología
/
Minería de Datos
/
Máquina de Vectores de Soporte
/
Métodos
Tipo de estudio:
Factores de riesgo
Límite:
Humanos
Idioma:
Coreano
Revista:
Korean Journal of Legal Medicine
Año:
2019
Tipo del documento:
Artículo
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