MOTIVATION: Genes silenced by the aberrent methylation of nearby CpG islands can contribute to the onset or progression of cancer and represent potential biomarkers for diagnosis and prognosis. Relatively few have thus far been validated as hypermethylated in cancer among over 14,000 candidates with promoter region CpG islands. A descriptive set of genes known to be unmethylated in cancer does not exist. This lack of a negative set and a large number of candidates necessitated the development of a new approach to identify novel genes hypermethylated in cancer. RESULTS: We developed a general method, cluster_boost, that in an imbalanced data setting predicts new minority class members given limited known samples and a large set of unlabeled samples. Synthetic datasets modeled after the hypermethylated genes data show that cluster_boost can successfully identify minority samples within unlabeled data. Using genome sequence features, cluster_boost predicted candidate hypermethylated genes among 14,000 genes of unknown status. In primary ovarian cancers, we determined the methylation status for 15 genes with different levels of support for being hypermethlyated. Results indicate cluster_boost can accurately identify novel genes hypermethylated in cancer. AVAILABILITY: Software and datasets are freely available at http://labs.genome.duke.edu/FureyLab/cluster_boost.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Genes silenced by the aberrent methylation of nearby CpG islands can contribute to the onset or progression of cancer and represent potential biomarkers for diagnosis and prognosis. Relatively few have thus far been validated as hypermethylated in cancer among over 14,000 candidates with promoter region CpG islands. A descriptive set of genes known to be unmethylated in cancer does not exist. This lack of a negative set and a large number of candidates necessitated the development of a new approach to identify novel genes hypermethylated in cancer. RESULTS: We developed a general method, cluster_boost, that in an imbalanced data setting predicts new minority class members given limited known samples and a large set of unlabeled samples. Synthetic datasets modeled after the hypermethylated genes data show that cluster_boost can successfully identify minority samples within unlabeled data. Using genome sequence features, cluster_boost predicted candidate hypermethylated genes among 14,000 genes of unknown status. In primary ovarian cancers, we determined the methylation status for 15 genes with different levels of support for being hypermethlyated. Results indicate cluster_boost can accurately identify novel genes hypermethylated in cancer. AVAILABILITY: Software and datasets are freely available at http://labs.genome.duke.edu/FureyLab/cluster_boost.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Meng Li; Hyun-il Henry Paik; Curt Balch; Yoosung Kim; Lang Li; Tim H-M Huang; Kenneth P Nephew; Sun Kim Journal: Bioinformatics Date: 2008-06-06 Impact factor: 6.937
Authors: G E Lind; C Raiborg; S A Danielsen; T O Rognum; E Thiis-Evensen; G Hoff; A Nesbakken; H Stenmark; R A Lothe Journal: Oncogene Date: 2011-04-18 Impact factor: 9.867