Paper Info
Title
Ranking Interactions for a Curation Task
Abstract
One of the key pieces of information which biomedical text mining systems are expected to extract from the literature are interactions among different types of biomedical entities (proteins, genes, diseases, drugs, etc.). Different types of entities might be considered, for example protein-protein interactions have been extensively studied as part of the Bio Creative competitive evaluations. However, more complex interactions such as those among genes, drugs, and diseases are increasingly of interest. Different databases have been used as reference for the evaluation of extraction and ranking techniques. The aim of this paper is to describe a machine-learning based reranking approach for candidate interactions extracted from the literature. The results are evaluated using data derived from the Pharm GKB database. The importance of a good ranking is particularly evident when the results are applied to support human curators.
Year
DOI
Venue
2011
10.1109/ICMLA.2011.119
ICMLA), 2011 10th International Conference
Keywords
DocType
Volume
data mining,learning (artificial intelligence),medical information systems,proteins,text analysis,BioCreative competitive evaluations,PharmGKB database,biomedical entities,biomedical text mining systems,candidate interaction extraction,curation task,information extraction,machine-learning based reranking approach,protein-protein interactions,ranking interactions,Literature Curation,Machine Learning,Maximum Entropy,Text Mining
Conference
2
ISBN
Citations 
PageRank 
978-1-4577-2134-2
3
0.41
References 
Authors
9
2
Name
Order
Citations
PageRank
Simon Clematide123227.86
Fabio Rinaldi2104.39