title: A hybrid computational method for the discovery of novel reproduction-related genes.
abstract: Uncovering the molecular mechanisms underlying reproduction is of great importance to infertility treatment and to the generation of healthy offspring. In this study, we discovered novel reproduction-related genes with a hybrid computational method, integrating three different types of method, which offered new clues for further reproduction research. This method was first executed on a weighted graph, constructed based on known protein-protein interactions, to search the shortest paths connecting any two known reproduction-related genes. Genes occurring in these paths were deemed to have a special relationship with reproduction. These newly discovered genes were filtered with a randomization test. Then, the remaining genes were further selected according to their associations with known reproduction-related genes measured by protein-protein interaction score and alignment score obtained by BLAST. The in-depth analysis of the high confidence novel reproduction genes revealed hidden mechanisms of reproduction and provided guidelines for further experimental validations.
authors.affiliation: College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, People's Republic of China.
authors.forename: Lei
authors.initials: L
authors.lastname: Chen
authors.affiliation: State Key Laboratory of Molecular Biology, Shanghai Key Laboratory of Molecular Andrology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, People's Republic of China.
authors.forename: Chen
authors.initials: C
authors.lastname: Chu
authors.affiliation: Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200025, People's Republic of China.
authors.forename: Xiangyin
authors.initials: X
authors.lastname: Kong
authors.affiliation: Institute of Systems Biology, Shanghai University, Shanghai, 200444, People's Republic of China.
authors.forename: Guohua
authors.initials: G
authors.lastname: Huang
authors.affiliation: Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200025, People's Republic of China.
authors.forename: Tao
authors.initials: T
authors.lastname: Huang
authors.affiliation: Institute of Systems Biology, Shanghai University, Shanghai, 200444, People's Republic of China.
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008957:Models, Genetic; D012098:Reproduction / Q000235:genetics*