TY - JOUR
T1 - The top-scoring 'N' algorithm
T2 - a generalized relative expression classification method from small numbers of biomolecules
AU - Magis, Andrew T.
AU - Price, Nathan D.
N1 - Funding Information:
The authors thank Dr. Don Geman and Bahman Afsari for valuable discussions during the development of this paper. This work was supported by a National Institutes of Health Howard Temin Pathway to Independence Award in Cancer Research [R00 CA126184]; the Camille Dreyfus Teacher-Scholar Program, and the Grand Duchy of Luxembourg-ISB Systems Medicine Consortium.
PY - 2012/9/11
Y1 - 2012/9/11
N2 - Background: Relative expression algorithms such as the top-scoring pair (TSP) and the top-scoring triplet (TST) have several strengths that distinguish them from other classification methods, including resistance to overfitting, invariance to most data normalization methods, and biological interpretability. The top-scoring 'N' (TSN) algorithm is a generalized form of other relative expression algorithms which uses generic permutations and a dynamic classifier size to control both the permutation and combination space available for classification.Results: TSN was tested on nine cancer datasets, showing statistically significant differences in classification accuracy between different classifier sizes (choices of N). TSN also performed competitively against a wide variety of different classification methods, including artificial neural networks, classification trees, discriminant analysis, k-Nearest neighbor, naïve Bayes, and support vector machines, when tested on the Microarray Quality Control II datasets. Furthermore, TSN exhibits low levels of overfitting on training data compared to other methods, giving confidence that results obtained during cross validation will be more generally applicable to external validation sets.Conclusions: TSN preserves the strengths of other relative expression algorithms while allowing a much larger permutation and combination space to be explored, potentially improving classification accuracies when fewer numbers of measured features are available.
AB - Background: Relative expression algorithms such as the top-scoring pair (TSP) and the top-scoring triplet (TST) have several strengths that distinguish them from other classification methods, including resistance to overfitting, invariance to most data normalization methods, and biological interpretability. The top-scoring 'N' (TSN) algorithm is a generalized form of other relative expression algorithms which uses generic permutations and a dynamic classifier size to control both the permutation and combination space available for classification.Results: TSN was tested on nine cancer datasets, showing statistically significant differences in classification accuracy between different classifier sizes (choices of N). TSN also performed competitively against a wide variety of different classification methods, including artificial neural networks, classification trees, discriminant analysis, k-Nearest neighbor, naïve Bayes, and support vector machines, when tested on the Microarray Quality Control II datasets. Furthermore, TSN exhibits low levels of overfitting on training data compared to other methods, giving confidence that results obtained during cross validation will be more generally applicable to external validation sets.Conclusions: TSN preserves the strengths of other relative expression algorithms while allowing a much larger permutation and combination space to be explored, potentially improving classification accuracies when fewer numbers of measured features are available.
KW - Classification
KW - Cross validation
KW - Graphics processing unit
KW - Microarray
KW - Relative expression
KW - Support vector machine
KW - Top-scoring pair
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U2 - 10.1186/1471-2105-13-227
DO - 10.1186/1471-2105-13-227
M3 - Article
C2 - 22966958
AN - SCOPUS:84865959225
SN - 1471-2105
VL - 13
JO - BMC Bioinformatics
JF - BMC Bioinformatics
IS - 1
M1 - 227
ER -