MinePhos: A Literature Mining System for Protein Phoshphorylation Information Extraction

Author

Yun Xu ; Da Teng ; Yiming Lei

Author_Institution

Anhui Province Key Lab. of High Performance Comput., Univ. of Sci. & Technol. of China, Hefei, China

Volume

9

Issue

1

fYear

2012

Firstpage

311

Lastpage

315

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.

Keywords

biochemistry; bioinformatics; cellular biophysics; data mining; molecular biophysics; proteins; support vector machines; MinePhos; RLIMS-P; SVM-based system; cellular process; data extraction; diseases; protein phosphorylation information extraction; Abstracts; Bioinformatics; Data mining; Databases; Dictionaries; Proteins; Substrates; Phospho.ELM; Phosphrylation; SVM; literature mining.; Computational Biology; Data Mining; Databases, Protein; Phosphorylation; Phosphotransferases; Proteins; Reproducibility of Results; Support Vector Machines;

fLanguage

English

Journal_Title

Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Publisher

ieee

ISSN

1545-5963

Type

jour

DOI

10.1109/TCBB.2011.85

Filename

5765938