Metrics based performance control over text mining tools in bioinformatics
Proceedings of the International Conference on Advances in Computing, Communication and Control
Assigning roles to protein mentions: The case of transcription factors
Journal of Biomedical Informatics
Support tools for literature-based information access in molecular biology
Proceedings of the 3rd International Universal Communication Symposium
Hybrid pattern matching for complex ontology term recognition
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
RankPref: ranking sentences describing relations between biomedical entities with an application
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
| Hi-index | 3.84 |
Summary: Search engines running on MEDLINE abstracts have been widely used by biologists to find publications that are related to their research. The existing search engines such as PubMed, however, have limitations when applied for the task of seeking textual evidence of relations between given concepts. The limitations are mainly due to the problem that the search engines do not effectively deal with multi-term queries which may imply semantic relations between the terms. To address this problem, we present MedEvi, a novel search engine that imposes positional restriction on occurrences matching multi-term queries, based on the observation that terms with semantic relations which are explicitly stated in text are not found too far from each other. MedEvi further identifies additional keywords of biological and statistical significance from local context of matching occurrences in order to help users reformulate their queries for better results. Availability: http://www.ebi.ac.uk/tc-test/textmining/medevi/ Contact: kim@ebi.ac.uk