Effective biomedical document classification for identifying publications relevant to the mouse Gene Expression Database (GXD)
| Author(s) | Jiang, Xiangying | |
| Author(s) | Ringwald, Martin | |
| Author(s) | Blake, Judith | |
| Author(s) | Shatkay, Hagit | |
| Ordered Author | Xiangying Jiang, Martin Ringwald, Judith Blake and Hagit Shatkay | |
| Ordered Author | ||
| UD Author | Jiang, Xiangying | en_US |
| UD Author | Shatkay, Hagit | en_US |
| Date Accessioned | 2018-06-07T14:37:31Z | |
| Date Available | 2018-06-07T14:37:31Z | |
| Copyright Date | Copyright © The Author(s) 2017 | en_US |
| Publication Date | 2017-03-24 | |
| Description | Publisher's PDF | en_US |
| Abstract | The Gene Expression Database (GXD) is a comprehensive online database within the Mouse Genome Informatics resource, aiming to provide available information about endogenous gene expression during mouse development. The information stems primarily from many thousands of biomedical publications that database curators must go through and read. Given the very large number of biomedical papers published each year, automatic document classification plays an important role in biomedical research. Specifically, an effective and efficient document classifier is needed for supporting the GXD annotation workflow. We present here an effective yet relatively simple classification scheme, which uses readily available tools while employing feature selection, aiming to assist curators in identifying publications relevant to GXD. We examine the performance of our method over a large manually curated dataset, consisting of more than 25 000 PubMed abstracts, of which about half are curated as relevant to GXD while the other half as irrelevant to GXD. In addition to text from title-and-abstract, we also consider image captions, an important information source that we integrate into our method. We apply a captions-based classifier to a subset of about 3300 documents, for which the full text of the curated articles is available. The results demonstrate that our proposed approach is robust and effectively addresses the GXD document classification. Moreover, using information obtained from image captions clearly improves performance, compared to title and abstract alone, affirming the utility of image captions as a substantial evidence source for automatically determining the relevance of biomedical publications to a specific subject area. | en_US |
| Department | University of Delaware.Department of Computer and Information Sciences. | en_US |
| Citation | Jiang, Xiangying, et al. "Effective biomedical document classification for identifying publications relevant to the mouse Gene Expression Database (GXD)." Database 2017.1 (2017). | en_US |
| DOI | 10.1093/database/bax017 | en_US |
| ISSN | 1758-0463 | en_US |
| URL | http://udspace.udel.edu/handle/19716/23552 | |
| Language | en_US | en_US |
| Publisher | Oxford University Press. | en_US |
| dc.rights | Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. | en_US |
| dc.source | Database: The Journal of Biological Databases and Curation | en_US |
| dc.source.uri | https://academic.oup.com/database | en_US |
| Title | Effective biomedical document classification for identifying publications relevant to the mouse Gene Expression Database (GXD) | en_US |
| Type | Article | en_US |
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