title: The use of Gene Ontology terms and KEGG pathways for analysis and prediction of oncogenes.
abstract: Oncogenes are a type of genes that have the potential to cause cancer. Most normal cells undergo programmed cell death, namely apoptosis, but activated oncogenes can help cells avoid apoptosis and survive. Thus, studying oncogenes is helpful for obtaining a good understanding of the formation and development of various types of cancers.
  
  
  
  In this study, we proposed a computational method, called OPM, for investigating oncogenes from the view of Gene Ontology (GO) and biological pathways. All investigated genes, including validated oncogenes retrieved from some public databases and other genes that have not been reported to be oncogenes thus far, were encoded into numeric vectors according to the enrichment theory of GO terms and KEGG pathways. Some popular feature selection methods, minimum redundancy maximum relevance and incremental feature selection, and an advanced machine learning algorithm, random forest, were adopted to analyze the numeric vectors to extract key GO terms and KEGG pathways.
  
  
  
  Along with the oncogenes, GO terms and KEGG pathways were discussed in terms of their relevance in this study. Some important GO terms and KEGG pathways were extracted using feature selection methods and were confirmed to be highly related to oncogenes. Additionally, the importance of these terms and pathways in predicting oncogenes was further demonstrated by finding new putative oncogenes based on them.
  
  
  
  This study investigated oncogenes based on GO terms and KEGG pathways. Some important GO terms and KEGG pathways were confirmed to be highly related to oncogenes. We hope that these GO terms and KEGG pathways can provide new insight for the study of oncogenes, particularly for building more effective prediction models to identify novel oncogenes. The program is available upon request.
  
  
  
  We hope that the new findings listed in this study may provide a new insight for the investigation of oncogenes. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.
authors.affiliation: Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China. Electronic address: xingzhihao@sibs.ac.cn.
authors.forename: Zhihao
authors.initials: Z
authors.lastname: Xing
authors.affiliation: Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China. Electronic address: chuchen@sibcb.ac.cn.
authors.forename: Chen
authors.initials: C
authors.lastname: Chu
authors.affiliation: College of Information Engineering, Shanghai Maritime University, Shanghai 201306, People's Republic of China. Electronic address: chen_lei1@163.com.
authors.forename: Lei
authors.initials: L
authors.lastname: Chen
authors.affiliation: Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, People's Republic of China. Electronic address: xykong@sibs.ac.cn.
authors.forename: Xiangyin
authors.initials: X
authors.lastname: Kong
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D063990:Gene Ontology; D006801:Humans; D009369:Neoplasms / Q000235:genetics*; D009857:Oncogenes / Q000235:genetics*; D015398:Signal Transduction / Q000235:genetics*
keywords: Gene Ontology; Incremental feature selection; KEGG pathway; Minimum redundancy maximum relevance; Oncogenes; Random forest