VDA, a method of choosing a better algorithm with fewer validations.
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 experim...
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2011-01-01
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doaj-d68b30de797741769d2f3d1a7c6c24ea2020-11-25T01:38:17ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-01610e2607410.1371/journal.pone.0026074VDA, a method of choosing a better algorithm with fewer validations.Francesco StrinoFabio ParisiYuval KlugerThe 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/http://europepmc.org/articles/PMC3192143?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Francesco Strino Fabio Parisi Yuval Kluger |
spellingShingle |
Francesco Strino Fabio Parisi Yuval Kluger VDA, a method of choosing a better algorithm with fewer validations. PLoS ONE |
author_facet |
Francesco Strino Fabio Parisi Yuval Kluger |
author_sort |
Francesco Strino |
title |
VDA, a method of choosing a better algorithm with fewer validations. |
title_short |
VDA, a method of choosing a better algorithm with fewer validations. |
title_full |
VDA, a method of choosing a better algorithm with fewer validations. |
title_fullStr |
VDA, a method of choosing a better algorithm with fewer validations. |
title_full_unstemmed |
VDA, a method of choosing a better algorithm with fewer validations. |
title_sort |
vda, a method of choosing a better algorithm with fewer validations. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2011-01-01 |
description |
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/ |
url |
http://europepmc.org/articles/PMC3192143?pdf=render |
work_keys_str_mv |
AT francescostrino vdaamethodofchoosingabetteralgorithmwithfewervalidations AT fabioparisi vdaamethodofchoosingabetteralgorithmwithfewervalidations AT yuvalkluger vdaamethodofchoosingabetteralgorithmwithfewervalidations |
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