title: Computational prediction of protein function based on weighted mapping of domains and GO terms.
abstract: In this paper, we propose a novel method, SeekFun, to predict protein function based on weighted mapping of domains and GO terms. Firstly, a weighted mapping of domains and GO terms is constructed according to GO annotations and domain composition of the proteins. The association strength between domain and GO term is weighted by symmetrical conditional probability. Secondly, the mapping is extended along the true paths of the terms based on GO hierarchy. Finally, the terms associated with resident domains are transferred to host protein and real annotations of the host protein are determined by association strengths. Our careful comparisons demonstrate that SeekFun outperforms the concerned methods on most occasions. SeekFun provides a flexible and effective way for protein function prediction. It benefits from the well-constructed mapping of domains and GO terms, as well as the reasonable strategy for inferring annotations of protein from those of its domains.
authors.affiliation: Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001, China ; Department of Information Management and Information System, Northeast Forestry University, Harbin 150001, China.
authors.forename: Zhixia
authors.initials: Z
authors.lastname: Teng
authors.affiliation: Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
authors.forename: Maozu
authors.initials: M
authors.lastname: Guo
authors.affiliation: Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
authors.forename: Qiguo
authors.initials: Q
authors.lastname: Dai
authors.affiliation: Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
authors.forename: Chunyu
authors.initials: C
authors.lastname: Wang
authors.affiliation: Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001, China ; Department of Statistical Genetics, Harbin Medical University, Harbin 150001, China.
authors.forename: Jin
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D015233:Models, Statistical; D011336:Probability; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000502:physiology*; D040901:Proteomics; D015203:Reproducibility of Results; D012984:Software
chemical_list: D011506:Proteins