BioContrasts: extracting and exploiting protein-protein contrastive relations from biomedical literature.
BACKGROUND
Contrasts are useful conceptual vehicles for learning processes and exploratory research of the unknown. For example, contrastive information between proteins can reveal what similarities, divergences and relations there are of the two proteins, leading to invaluable insights for better understanding about the proteins. Such contrastive information are found to be reported in the biomedical literature. However, there have been no reported attempts in current biomedical text mining work that systematically extract and present such useful contrastive information from the literature for exploitation.


RESULTS
Our BioContrasts system extracts protein-protein contrastive information from MEDLINE abstracts and presents the information to biologists in a web-application for exploitation. Contrastive information are identified in the text abstracts with contrastive negation patterns such as 'A but not B'. A total of 799 169 pairs of contrastive expressions were successfully extracted from 2.5 million MEDLINE abstracts. Using grounding of contrastive protein names to Swiss-Prot entries, we were able to produce 41 471 pieces of contrasts between Swiss-Prot protein entries. These contrastive pieces of information are then presented via a user-friendly interactive web portal that can be exploited for applications such as the refinement of biological pathways.


BACKGROUND
BioContrasts can be accessed at http://biocontrasts.i2r.a-star.edu.sg. It is also mirrored at http://biocontrasts.biopathway.org.


BACKGROUND
Supplementary materials are available at Bioinformatics online.
Kim
Jung-Jae
JJ
Computer Science Division & AITrc, Korea Advanced Institute of Science and Technology, Yuseong-gu, Daejeon 305-701, South Korea.
Zhang
Zhuo
Z
Park
Jong C
JC
Ng
See-Kiong
SK
D001185:Artificial Intelligence; D035843:Biomedical Research / Q000379:methods; D003628:Database Management Systems; D016208:Databases, Factual; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D010506:Periodicals as Topic; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000145:classification* / Q000378:metabolism*; D012984:Software; D014584:User-Computer Interface
D011506:Proteins