k-Nearest neighbor models for microarray gene expression analysis and clinical outcome prediction.
Pharmacogenomics J
; 10(4): 292-309, 2010 Aug.
Article
en En
| MEDLINE
| ID: mdl-20676068
ABSTRACT
In the clinical application of genomic data analysis and modeling, a number of factors contribute to the performance of disease classification and clinical outcome prediction. This study focuses on the k-nearest neighbor (KNN) modeling strategy and its clinical use. Although KNN is simple and clinically appealing, large performance variations were found among experienced data analysis teams in the MicroArray Quality Control Phase II (MAQC-II) project. For clinical end points and controls from breast cancer, neuroblastoma and multiple myeloma, we systematically generated 463,320 KNN models by varying feature ranking method, number of features, distance metric, number of neighbors, vote weighting and decision threshold. We identified factors that contribute to the MAQC-II project performance variation, and validated a KNN data analysis protocol using a newly generated clinical data set with 478 neuroblastoma patients. We interpreted the biological and practical significance of the derived KNN models, and compared their performance with existing clinical factors.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Modelos Estadísticos
/
Análisis de Secuencia por Matrices de Oligonucleótidos
Tipo de estudio:
Etiology_studies
/
Guideline
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Animals
/
Female
/
Humans
Idioma:
En
Revista:
Pharmacogenomics J
Asunto de la revista:
BIOLOGIA MOLECULAR
/
FARMACOLOGIA
Año:
2010
Tipo del documento:
Article
País de afiliación:
Estados Unidos