We describe a system that extracts disease-gene relations from M edLine. We constructed a dictionary for disease and gene names from six public databases and extracted relation candidates by dictionary matching. Since dictionary matching produces a large number of false positives, we developed a method of machine learning-based named entity recognition (NER) to filter out false recognitions of disease/gene names. We found that the performance of relation extraction is heavily dependent upon the performance of NER filtering and that the filtering improves the precision of relation extraction by 26.7% at the cost of a small reduction in recall.
The improvement of the precision rate was often at the cost of deteriorating the recall rate REF .
Extraction of Gene-Disease Relations from Medline Using Domain Dictionaries and Machine Learning
Pacific Symposium on Biocomputing
Computer Science Medicine