Abstract
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 protein-protein interactions (DIPPPI). The system reports an F-Measure of 0.55 on test MEDLINE abstracts.
| Original language | English |
|---|---|
| Pages | 89-96 |
| Number of pages | 8 |
| Publication status | Published - 1 Jan 2007 |
| Event | ACL 2007 Workshop on Biological, Translational, and Clinical Language Processing, BioNLP 2007 - Prague, Czech Republic Duration: 29 Jun 2007 → … |
Other
| Other | ACL 2007 Workshop on Biological, Translational, and Clinical Language Processing, BioNLP 2007 |
|---|---|
| Country | Czech Republic |
| City | Prague |
| Period | 29/6/07 → … |
Fingerprint
ASJC Scopus subject areas
- Language and Linguistics
- Information Systems
- Software
- Health Informatics
- Computer Science Applications
- Biomedical Engineering
Cite this
BioNoculars : Extracting protein-protein interactions from biomedical text. / Madkour, Amgad; Darwish, Kareem; Hassan, Hany; Hassan, Ahmed; Emam, Ossama.
2007. 89-96 Paper presented at ACL 2007 Workshop on Biological, Translational, and Clinical Language Processing, BioNLP 2007, Prague, Czech Republic.Research output: Contribution to conference › Paper
}
TY - CONF
T1 - BioNoculars
T2 - Extracting protein-protein interactions from biomedical text
AU - Madkour, Amgad
AU - Darwish, Kareem
AU - Hassan, Hany
AU - Hassan, Ahmed
AU - Emam, Ossama
PY - 2007/1/1
Y1 - 2007/1/1
N2 - 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 protein-protein interactions (DIPPPI). The system reports an F-Measure of 0.55 on test MEDLINE abstracts.
AB - 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 protein-protein interactions (DIPPPI). The system reports an F-Measure of 0.55 on test MEDLINE abstracts.
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M3 - Paper
AN - SCOPUS:85037353106
SP - 89
EP - 96
ER -