title: MinePhos: a literature mining system for protein phoshphorylation information extraction.
abstract: The rapid growth of scientific literature calls for automatic and efficient ways to facilitate extracting experimental data on protein phosphorylation. Such information is of great value for biologists in studying cellular processes and diseases such as cancer and diabetes. Existing approaches like RLIMS-P are mainly rule based. The performance lays much reliance on the completeness of rules. We propose an SVM-based system known as MinePhos which outperforms RLIMS-P in both precision and recall of information extraction when tested on a set of articles randomly chosen from PubMed.
authors.affiliation: University of Science and Technology of China, Hefei and Anhui Province Key Laboratory of High Performance Computing, Hefei.
authors.forename: Yun
authors.initials: Y
authors.lastname: Xu
authors.forename: Da
authors.initials: D
authors.lastname: Teng
authors.forename: Yiming
authors.initials: Y
authors.lastname: Lei
mesh_terms: D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D030562:Databases, Protein; D010766:Phosphorylation; D010770:Phosphotransferases; D011506:Proteins / Q000737:chemistry / Q000145:classification; D015203:Reproducibility of Results; D060388:Support Vector Machine
chemical_list: D011506:Proteins; D010770:Phosphotransferases