Paper Info
Title
BioPubMiner: machine learning component-based biomedical information analysis platform
Abstract
In this paper we introduce BioPubMiner, a machine learning component-based platform for biomedical information analysis. BioPubMiner employs natural language processing techniques and machine learning based data mining techniques for mining useful biological information such as protein-protein interaction from the massive literature. The system recognizes biological terms such as gene, protein, and enzymes and extracts their interactions described in the document through natural language processing. The extracted interactions are further analyzed with a set of features of each entity that were collected from the related public database to infer more interactions from the original interactions. The performance of entity and interaction extraction was tested with selected MEDLINE abstracts. The evaluation of inference proceeded using the protein interaction data of S.cerevisiae (bakers yeast) from MIPS and SGD.
Year
DOI
Venue
2004
10.1007/978-3-540-30561-3_2
CIT
Keywords
Field
DocType
component-based biomedical information analysis,natural language processing technique,useful biological information,protein-protein interaction,natural language processing,protein interaction data,original interaction,biological term,interaction extraction,data mining technique,biomedical information analysis,information analysis,data mining,machine learning,protein protein interaction,enzyme
Inference,Computer science,Document processing,Information extraction,Machine code,Natural language,Association rule learning,Artificial intelligence,Component-based software engineering,Bakers Yeast,Machine learning
Conference
Volume
ISSN
ISBN
3356
0302-9743
3-540-24126-4
Citations 
PageRank 
References 
0
0.34
15
Authors
2
Name
Order
Citations
PageRank
Jae-Hong Eom1868.91
Byoung-Tak Zhang21571158.56