A graph-search framework for associating gene identifiers with documents |
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| Authors: | William W Cohen Einat Minkov |
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| Institution: | (1) Department of Machine Learning, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, USA;(2) Language Technology Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA;(3) Center for Bioimage Informatics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA |
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| 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. |
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