Improving Cancer Classification Accuracy Using Gene Pairs

• Published in: PloS one Vol. 5; no. 12; p. e14305
• Main Authors: Chopra, Pankaj, Lee, Jinseung, Kang, Jaewoo, Lee, Sunwon
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 21.12.2010; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Artificial intelligence; Bayes Theorem; Bayesian analysis; Bioindicators; Bioinformatics; Biomarkers; Biomarkers, Tumor; Cancer; Cancer genetics; Carcinogenesis; Carcinogens; Classification; Communication; Comparative analysis; Computational Biology; Computational Biology/Transcriptional Regulation; Computer science; Computer Science/Information Technology; Data mining; Datasets; Decision analysis; Decision trees; Deregulation; Diagnostic tests; DNA microarrays; Gene expression; Gene Expression Regulation, Neoplastic; Genes; Humans; International conferences; Lung cancer; Lymphoma; Lymphomas; Medical diagnosis; Mesothelioma; Methods; Models, Biological; Models, Genetic; Models, Statistical; Neoplasms - classification; Neoplasms - diagnosis; Nervous system; Oligonucleotide Array Sequence Analysis; Prostate cancer; Reproducibility of Results; Sequence Analysis, DNA - methods; Support vector machines; Washington DC; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0014305

Recent studies suggest that the deregulation of pathways, rather than individual genes, may be critical in triggering carcinogenesis. The pathway deregulation is often caused by the simultaneous deregulation of more than one gene in the pathway. This suggests that robust gene pair combinations may exploit the underlying bio-molecular reactions that are relevant to the pathway deregulation and thus they could provide better biomarkers for cancer, as compared to individual genes. In order to validate this hypothesis, in this paper, we used gene pair combinations, called doublets, as input to the cancer classification algorithms, instead of the original expression values, and we showed that the classification accuracy was consistently improved across different datasets and classification algorithms. We validated the proposed approach using nine cancer datasets and five classification algorithms including Prediction Analysis for Microarrays (PAM), C4.5 Decision Trees (DT), Naive Bayesian (NB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN).