BioNoculars: Extracting Protein-Protein Interactions from Biomedical Text
نویسندگان
چکیده
The vast number of published medical documents is considered a vital source for relationship discovery. This paper presents a statistical unsupervised system, called BioNoculars, for extracting protein-protein interactions from biomedical text. BioNoculars uses graph-based mutual reinforcement to make use of redundancy in data to construct extraction patterns in a domain independent fashion. The system was tested using MEDLINE abstract for which the protein-protein interactions that they contain are listed in the database of interacting proteins and proteinprotein interactions (DIPPPI). The system reports an F-Measure of 0.55 on test MEDLINE abstracts.
منابع مشابه
Extracting PPIs from MEDLINE using the HVS Model 1 Extracting Protein-Protein Interactions from MEDLINE using the Hidden Vector State Model
Protein-protein interactions referring to the associations of protein molecules are crucial for many biological functions. A major challenge in text mining for biomedicine is automatically extracting protein-protein interactions from the vast amount of biomedical literature since most knowledge about them still hides in biomedical publications. We have constructed an information extraction syst...
متن کاملCollection-Wide Extraction of Protein-Protein Interactions
Evidence in support of relationships among biomedical entities, such as protein-protein interactions, can be gathered from a multiplicity of sources. The larger the pool of evidence, the more likely a given interaction can be considered to be. In the context of biomedical text mining, this elementary observation can be translated into an approach that seeks to find in the literature all availab...
متن کاملFrom Biomedical Literature to Knowledge: Mining Protein-Protein Interactions
To date, more than 16 million citations of published articles in biomedical domain are available in the MEDLINE database. These articles describe the new discoveries which accompany a tremendous development in biomedicine during the last decade. It is crucial for biomedical researchers to retrieve and mine some specific knowledge from the huge quantity of published articles with high efficiency...
متن کاملComparative experiments on learning information extractors for proteins and their interactions
OBJECTIVE Automatically extracting information from biomedical text holds the promise of easily consolidating large amounts of biological knowledge in computer-accessible form. This strategy is particularly attractive for extracting data relevant to genes of the human genome from the 11 million abstracts in Medline. However, extraction efforts have been frustrated by the lack of conventions for...
متن کاملExtracting Protein-Protein Interactions from MEDLINE using the Hidden Vector State model
A major challenge in text mining for biomedicine is automatically extracting protein-protein interactions from the vast amount of biomedical literature. We have constructed an information extraction system based on the Hidden Vector State (HVS) model for protein-protein interactions. The HVS model can be trained using only lightly annotated data whilst simultaneously retaining sufficient abilit...
متن کامل