| Literature DB >> 30521143 |
Mansheng Li1, Qiang He2, Jie Ma1, Fuchu He1, Yunping Zhu1, Cheng Chang1, Tao Chen1.
Abstract
Protein-protein interaction extraction through biological literature curation is widely employed for proteome analysis. There is a strong need for a tool that can assist researchers in extracting comprehensive PPI information through literature curation, which is critical in research on protein, for example, construction of protein interaction network, identification of protein signaling pathway, and discovery of meaningful protein interaction. However, most of current tools can only extract PPI relations. None of them are capable of extracting other important PPI information, such as interaction directions, effects, and functional annotations. To address these issues, this paper proposes PPICurator, a novel tool for extracting comprehensive PPI information with a variety of logic and syntax features based on a new support vector machine classifier. PPICurator provides a friendly web-based user interface. It is a platform that automates the extraction of comprehensive PPI information through literature, including PPI relations, as well as their confidential scores, interaction directions, effects, and functional annotations. Thus, PPICurator is more comprehensive than state-of-the-art tools. Moreover, it outperforms state-of-the-art tools in the accuracy of PPI relation extraction measured by F-score and recall on the widely used open datasets. PPICurator is available at https://ppicurator.hupo.org.cn.Keywords: biological network; gene ontology; protein-protein interaction; text mining
Mesh:
Year: 2019 PMID: 30521143 DOI: 10.1002/pmic.201800291
Source DB: PubMed Journal: Proteomics ISSN: 1615-9853 Impact factor: 3.984