{"id":332891,"date":"2016-12-07T10:53:31","date_gmt":"2016-12-07T18:53:31","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=332891"},"modified":"2018-10-16T19:58:21","modified_gmt":"2018-10-17T02:58:21","slug":"bionoculars-extracting-protein-protein-interactions-biomedical-text","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/bionoculars-extracting-protein-protein-interactions-biomedical-text\/","title":{"rendered":"BioNoculars: Extracting Protein-Protein Interactions from Biomedical Text"},"content":{"rendered":"

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.<\/p>\n","protected":false},"excerpt":{"rendered":"

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. 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