k-Nearest neighbor models for microarray gene expression analysis and clinical outcome prediction.
Pharmacogenomics J
; 10(4): 292-309, 2010 Aug.
Article
em 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
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
/
Análise de Sequência com Séries de Oligonucleotídeos
Tipo de estudo:
Etiology_studies
/
Guideline
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
/
Female
/
Humans
Idioma:
En
Revista:
Pharmacogenomics J
Assunto da revista:
BIOLOGIA MOLECULAR
/
FARMACOLOGIA
Ano de publicação:
2010
Tipo de documento:
Article
País de afiliação:
Estados Unidos