title: An integrated method for the identification of novel genes related to oral cancer.
abstract: Cancer is a significant public health problem worldwide. Complete identification of genes related to one type of cancer facilitates earlier diagnosis and effective treatments. In this study, two widely used algorithms, the random walk with restart algorithm and the shortest path algorithm, were adopted to construct two parameterized computational methods, namely, an RWR-based method and an SP-based method; based on these methods, an integrated method was constructed for identifying novel disease genes. To validate the utility of the integrated method, data for oral cancer were used, on which the RWR-based and SP-based methods were trained, thereby building two optimal methods. The integrated method combining these optimal methods was further adopted to identify the novel genes of oral cancer. As a result, 85 novel genes were inferred, among which eleven genes (e.g., MYD88, FGFR2, NF-κBIA) were identified by both the RWR-based and SP-based methods, 70 genes (e.g., BMP4, IFNG, KITLG) were discovered only by the RWR-based method and four genes (L1R1, MCM6, NOG and CXCR3) were predicted only by the SP-based method. Extensive analyses indicate that several novel genes have strong associations with cancers, indicating the effectiveness of the integrated method for identifying disease genes.
authors.affiliation: School of Life Sciences, Shanghai University, Shanghai, People's Republic of China.|College of Information Engineering, Shanghai Maritime University, Shanghai, People's Republic of China.
authors.forename: Lei
authors.initials: L
authors.lastname: Chen
authors.affiliation: School of Life Sciences, Shanghai University, Shanghai, People's Republic of China.
authors.forename: Jing
authors.initials: J
authors.lastname: Yang
authors.affiliation: Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China.
authors.forename: Zhihao
authors.initials: Z
authors.lastname: Xing
authors.affiliation: Department of Science & Technology, Binzhou Medical University Hospital, Binzhou, Shandong, People's Republic of China.
authors.forename: Fei
authors.initials: F
authors.lastname: Yuan
authors.affiliation: Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Shu
authors.affiliation: School of Resources and Environment, Anhui Agricultural University, Hefei, Anhui, People's Republic of China.
authors.forename: YunHua
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China.
authors.forename: XiangYin
authors.initials: X
authors.lastname: Kong
authors.affiliation: Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China.
authors.forename: Tao
authors.initials: T
authors.lastname: Huang
authors.affiliation: CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, People's Republic of China.
authors.forename: HaiPeng
authors.initials: H
authors.lastname: Li
authors.affiliation: School of Life Sciences, Shanghai University, Shanghai, People's Republic of China.
authors.forename: Yu-Dong
authors.identifier: http://orcid.org/0000-0001-5664-7979
authors.initials: YD
authors.lastname: Cai
mesh_terms: D000465:Algorithms; D006801:Humans; D009062:Mouth Neoplasms / Q000235:genetics*