Detecting novel hypermethylated genes in breast cancer benefiting from feature selection.
The aberrant hypermethylation of CpG islands in promoter regions of genes plays an important role in the onset and progression of Breast cancer. Meanwhile, it is highly associated with human genomic features. Two feature selection algorithms: t-test and CfsSubsetEval were used to obtain efficient feature subsets. We discovered 14 significant feature subsets by CfsSubsetEval, which can distinguish hypermethylated genes from control genes. As a result, 393 unconfirmed hypermethylated genes in Breast cancer were prioritized. These genes were assigned the hypermethylated scores and were supported by literature and Gene Ontology enrichment. This paper suggests that the feature subsets could be served as discriminating genomic markers to infer novel hypermethylated genes in cancer potentially.
Lv
Jie
J
Harbin Medical University, China.
Su
Jianzhong
J
Wang
Fang
F
Qi
Yunfeng
Y
Liu
Hongbo
H
Zhang
Yan
Y
D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000235:genetics* / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D018899:CpG Islands / Q000235:genetics; D019175:DNA Methylation / Q000235:genetics*; D004273:DNA, Neoplasm / Q000378:metabolism*; D030541:Databases, Genetic; D005260:Female; D052138:Genes, Neoplasm / Q000235:genetics*; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics; D011401:Promoter Regions, Genetic / Q000235:genetics; D012372:ROC Curve; D012984:Software
D004273:DNA, Neoplasm