title: Improving cancer classification accuracy using gene pairs.
abstract: 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).
authors.affiliation: Department of Human Genetics, School of Medicine, Emory University, Atlanta, Georgia, United States of America.
authors.forename: Pankaj
authors.initials: P
authors.lastname: Chopra
authors.forename: Jinseung
authors.initials: J
authors.lastname: Lee
authors.forename: Jaewoo
authors.initials: J
authors.lastname: Kang
authors.forename: Sunwon
authors.initials: S
authors.lastname: Lee
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D014408:Biomarkers, Tumor; D019295:Computational Biology; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008954:Models, Biological; D008957:Models, Genetic; D015233:Models, Statistical; D009369:Neoplasms / Q000145:classification* / Q000175:diagnosis*; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D017422:Sequence Analysis, DNA / Q000379:methods
chemical_list: D014408:Biomarkers, Tumor