Extracting regulatory gene expression networks from PubMed
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Kernel approaches for genic interaction extraction
Bioinformatics
High-performance gene name normalization with GeNo
Bioinformatics
Learning relations from biomedical corpora using dependency trees
KDECB'06 Proceedings of the 1st international conference on Knowledge discovery and emergent complexity in bioinformatics
Towards automatic pathway generation from biological full-text publications
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
| Hi-index | 0.00 |
We explore a rule system and a machine learning (ML) approach to automatically harvest information on gene regulation events (GREs) from biological documents in two different evaluation scenarios -- one uses self-supplied corpora in a clean lab setting, while the other incorporates a standard reference database of curated GREs from RegulonDB, real-life data generated independently from our work. In the lab condition, we test how feasible the automatic extraction of GREs really is and achieve F-scores, under different, not directly comparable test conditions though, for the rule and the ML systems which amount to 34% and 44%, respectively. In the RegulonDB condition, we investigate how robust both methodologies are by comparing them with this routinely used database. Here, the best F-scores for the rule and the ML systems amount to 34% and 19%, respectively.