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

Andrew T. Magis, Nathan D. Price

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number227
JournalBMC Bioinformatics
Volume13
Issue number1
DOIs
StatePublished - Sep 11 2012
Externally publishedYes

Keywords

  • Classification
  • Cross validation
  • Graphics processing unit
  • Microarray
  • Relative expression
  • Support vector machine
  • Top-scoring pair

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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