VDA, a method of choosing a better algorithm with fewer validations.
PLoS One
; 6(10): e26074, 2011.
Article
en En
| MEDLINE
| ID: mdl-22046256
ABSTRACT
The multitude of bioinformatics algorithms designed for performing a particular computational task presents end-users with the problem of selecting the most appropriate computational tool for analyzing their biological data. The choice of the best available method is often based on expensive experimental validation of the results. We propose an approach to design validation sets for method comparison and performance assessment that are effective in terms of cost and discrimination power.Validation Discriminant Analysis (VDA) is a method for designing a minimal validation dataset to allow reliable comparisons between the performances of different algorithms. Implementation of our VDA approach achieves this reduction by selecting predictions that maximize the minimum Hamming distance between algorithmic predictions in the validation set. We show that VDA can be used to correctly rank algorithms according to their performances. These results are further supported by simulations and by realistic algorithmic comparisons in silico.VDA is a novel, cost-efficient method for minimizing the number of validation experiments necessary for reliable performance estimation and fair comparison between algorithms.Our VDA software is available at http//sourceforge.net/projects/klugerlab/files/VDA/
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Validación de Programas de Computación
/
Biología Computacional
/
Estudios de Validación como Asunto
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
PLoS One
Asunto de la revista:
CIENCIA
/
MEDICINA
Año:
2011
Tipo del documento:
Article
País de afiliación:
Estados Unidos