Construction of robust prognostic predictors by using projective adaptive resonance theory as a gene filtering method

Bioinformatics. 2005 Jan 15;21(2):179-86. doi: 10.1093/bioinformatics/bth473. Epub 2004 Aug 12.


Motivation: For establishing prognostic predictors of various diseases using DNA microarray analysis technology, it is desired to find selectively significant genes for constructing the prognostic model and it is also necessary to eliminate non-specific genes or genes with error before constructing the model.

Results: We applied projective adaptive resonance theory (PART) to gene screening for DNA microarray data. Genes selected by PART were subjected to our FNN-SWEEP modeling method for the construction of a cancer class prediction model. The model performance was evaluated through comparison with a conventional screening signal-to-noise (S2N) method or nearest shrunken centroids (NSC) method. The FNN-SWEEP predictor with PART screening could discriminate classes of acute leukemia in blinded data with 97.1% accuracy and classes of lung cancer with 90.0% accuracy, while the predictor with S2N was only 85.3 and 70.0% or the predictor with NSC was 88.2 and 90.0%, respectively. The results have proven that PART was superior for gene screening.

Availability: The software is available upon request from the authors.


Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.
  • Validation Study

MeSH terms

  • Algorithms*
  • Biomarkers, Tumor / genetics*
  • Breast Neoplasms / diagnosis*
  • Breast Neoplasms / genetics*
  • Clinical Trials as Topic
  • Gene Expression Profiling / methods*
  • Gene Expression Regulation, Neoplastic
  • Genetic Testing / methods*
  • Genetic Variation
  • Humans
  • Internet
  • Leukemia / diagnosis
  • Leukemia / genetics
  • Lung Neoplasms / diagnosis
  • Lung Neoplasms / genetics
  • Neoplasm Proteins / genetics*
  • Oligonucleotide Array Sequence Analysis / methods*
  • Prognosis
  • Reproducibility of Results
  • Sample Size
  • Sensitivity and Specificity
  • Software
  • Treatment Outcome


  • Biomarkers, Tumor
  • Neoplasm Proteins