Assisting manual literature curation for protein-protein interactions using BioQRator

Abstract

The time-consuming nature of manual curation and the rapid growth of biomedical literature severely limit the number of articles that database curators can scrutinize and annotate. Hence, semi-automatic tools can be a valid support to increase annotation throughput. Although a handful of curation assistant tools are already available, to date, little has been done to formally evaluate their benefit to biocuration. Moreover, most curation tools are designed for specific problems. Thus, it is not easy to apply an annotation tool for multiple tasks. BioQRator is a publicly available web-based tool for annotating biomedical literature. It was designed to support general tasks, i.e. any task annotating entities and relationships. In the BioCreative IV edition, BioQRator was tailored for protein– protein interaction (PPI) annotation by migrating information from PIE the search. The results obtained from six curators showed that the precision on the top 10 documents doubled with PIE the search compared with PubMed search results. It was also observed that the annotation time for a full PPI annotation task decreased for a beginner-intermediate level annotator. This finding is encouraging because text-mining techniques were not directly involved in the full annotation task and BioQRator can be easily integrated with any text-mining resources.Database URL: http://www.bioqrator.org/

The BioGRID interaction database: 2017 update

“…The BioGRID curation pipeline will be enhanced through the integration of ever more sophisticated text-mining tools, which will be implemented in collaboration with text-mining groups and the BioCreative consortium (19,48,49,55,79,80). These efforts will be augmented by collaborations with diverse database partners, including MODs, phenotype databases, and chemical databases.…”

The BioGRID interaction database: 2015 update

“…We have also begun to use text-mining for the curation of protein phosphorylation sites through a collaboration with developers of the RLIMS-P system ( 57 ). To facilitate the development of improved text-mining approaches, the BioGRID routinely contributes to the BioCreative (Critical Assessment of Information Extraction in Biology) challenge by providing test datasets and curation expertise ( 58 – 60 ).…”

Progress of research in machine learning-based protein-protein interaction extraction

“…iHOP [83,84] Web server/ services http://www.ihop-net.org/UniPub/iHOP/ 2004 PPIExtractor [85] Standalone http://202.118.75.18:8080/PPIExtractor 2011 PPI Finder [86] Web server http://liweilab.genetics.ac.cn/tm/ 2009 PolySearch [87] Web server http://wishart.biology.ualberta.ca/polysearch/ 2008 PIE the search [88] Web server http://www.ncbi.nlm.nih.gov/IRET/PIE/ 2012 PPLook [89] Web server/ Standalone http://meta.usc.edu/softs/PPLook/ 2010 ReactionExtractor [90] Standalone  2012 PPInterFinder [91] Web server http://www.biominingbu.org/ppinterfinder 2013 PCorral [92] Web server http://www.ebi.ac.uk/Rebholz-srv/pcorral/ 2013 BioQRator [93] Web server http://www.bioqrator.org/ 2014 域. 由于对生物语料进行人工标注除了耗时费力还 需要专业的背景知识, 代价很高.…”