The top-scoring ‘N’ algorithm: a generalized relative expression classification method from small numbers of biomolecules

BMC Bioinformatics

Methodology Article

Andrew T Magis¹  Nathan D Price¹ 

[1] Institute for Systems Biology, 401 Terry Ave N, 98109, Seattle, WA, USA;Center for Biophysics and Computational Biology, University of Illinois, 61801, Urbana, IL, USA;

关键词: Classification;    Top-scoring pair;    Relative expression;    Cross validation;    Support vector machine;    Graphics processing unit;    Microarray;   

DOI  :  10.1186/1471-2105-13-227

 received in 2012-02-19, accepted in 2012-09-03,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundRelative 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.ResultsTSN 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.ConclusionsTSN 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.

【 授权许可】

CC BY   
© Magis and Price; licensee BioMed Central Ltd. 2012