A feature selection method for classification within functional genomics experiments based on the proportional overlapping score.
BMC Bioinformatics
; 15: 274, 2014 Aug 11.
Article
en En
| MEDLINE
| ID: mdl-25113817
ABSTRACT
BACKGROUND:
Microarray technology, as well as other functional genomics experiments, allow simultaneous measurements of thousands of genes within each sample. Both the prediction accuracy and interpretability of a classifier could be enhanced by performing the classification based only on selected discriminative genes. We propose a statistical method for selecting genes based on overlapping analysis of expression data across classes. This method results in a novel measure, called proportional overlapping score (POS), of a feature's relevance to a classification task.RESULTS:
We apply POS, along-with four widely used gene selection methods, to several benchmark gene expression datasets. The experimental results of classification error rates computed using the Random Forest, k Nearest Neighbor and Support Vector Machine classifiers show that POS achieves a better performance.CONCLUSIONS:
A novel gene selection method, POS, is proposed. POS analyzes the expressions overlap across classes taking into account the proportions of overlapping samples. It robustly defines a mask for each gene that allows it to minimize the effect of expression outliers. The constructed masks along-with a novel gene score are exploited to produce the selected subset of genes.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Perfilación de la Expresión Génica
/
Genómica
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
BMC Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2014
Tipo del documento:
Article
País de afiliación:
Reino Unido