Journal article

GeneRIF indexing: sentence selection based on machine learning

Antonio J Jimeno-Yepes, J Caitlin Sticco, James G Mork, Alan R Aronson

BMC BIOINFORMATICS | BMC | Published : 2013

Abstract

BACKGROUND: A Gene Reference Into Function (GeneRIF) describes novel functionality of genes. GeneRIFs are available from the National Center for Biotechnology Information (NCBI) Gene database. GeneRIF indexing is performed manually, and the intention of our work is to provide methods to support creating the GeneRIF entries. The creation of GeneRIF entries involves the identification of the genes mentioned in MEDLINE®; citations and the sentences describing a novel function. RESULTS: We have compared several learning algorithms and several features extracted or derived from MEDLINE sentences to determine if a sentence should be selected for GeneRIF indexing. Features are derived from the sent..

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Grants

Funding Acknowledgements

This work was supported in part by the Intramural Research Program of the NIH, National Library of Medicine and by an appointment of A. Jimeno-Yepes to the NLM Research Participation Program sponsored by the National Library of Medicine and administered by the Oak Ridge Institute for Science and Education.NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program.