Gene function classification using NCI-60 cell line gene expression profiles

  • Authors:

  • Daijin Ko;Wanyan Xu;Brad Windle

  • Affiliations:

  • Department of Management Science and Statistics, School of Business, University of Texas at San Antonio, 6900 North Loop 1604 West, San Antonio, TX 78249, USA;Department of Medicinal Chemistry, School of Pharmacy, P.O. Box 980540, Virginia Commonwealth University, Richmond, VA 23298, USA;Department of Medicinal Chemistry, School of Pharmacy, P.O. Box 980540, Virginia Commonwealth University, Richmond, VA 23298, USA and Massey Cancer Center, P.O. Box 980037, Virginia Commonwealth U ...

  • Venue:
  • Computational Biology and Chemistry
  • Year:
  • 2005

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Abstract

Gene expression patterns from NCI's panel of 60 cell lines were used to train a Neural Network model for classifying genes to pathways. The model assigns probabilities to each gene for each of the 21 modeled pathways assigned by the Kyoto Encyclopedia of Genes and Genomes. Cross-validation of the model showed that 10 of the 21 pathways exhibited good performance in statistical significance and accuracy. The model was designed to output gene probabilities that could be screened for higher probabilities resulting in higher confidence in classification though yielding fewer genes per pathway. The model was deployed on 5798 genes and our approach allowed us to ascertain the most relevant genes above an estimated background. Eight pathways were identified with both good cross-validation and significant numbers above background, TCA Cycle, Oxidative Phosphorylation, Porphyrin Biosynthesis, Ribosome, Polymerases, Proteasome, Cell Cycle, and Cell Adhesion. Gene Ontology (GO) annotation was used for additional validation of gene classification results. A total of 551 GO annotated genes and 468 unannotated genes were classified to the 8 pathways. The primary and secondary classifications of genes revealed known pathway relationships and provide the potential for discovering new pathway relationships.