A graph-search framework for associating gene identifiers with documents.

ABSTRACT:

Background

One step in the model organism database curation process is to find, for each article, the identifier of every gene discussed in the article. We consider a relaxation of this problem suitable for semi-automated systems, in which each article is associated with a ranked list of possible gene identifiers, and experimentally compare methods for solving this geneId ranking problem. In addition to baseline approaches based on combining named entity recognition (NER) systems with a "soft dictionary" of gene synonyms, we evaluate a graph-based method which combines the outputs of multiple NER systems, as well as other sources of information, and a learning method for reranking the output of the graph-based method.

Results

We show that named entity recognition (NER) syste

SUBMITTER: Cohen WW 

PROVIDER: S-EPMC1617121 | biostudies-literature | 2006 Oct

REPOSITORIES: biostudies-literature