Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 342194
Title CoPub Mapper: mining MEDLINE based on search term co-publication
Author(s) Alako, B.T.F.; Veldhoven, A.; Baal, S. van; Jelier, R.; Verhoeven, S.; Rullman, T.; Polman, J.; Jenster, G.
Source BMC Bioinformatics 6 (2005). - ISSN 1471-2105 - 15 p.
DOI https://doi.org/10.1186/1471-2105-6-51
Department(s) Bioinformatics
EPS-4
Publication type Refereed Article in a scientific journal
Publication year 2005
Keyword(s) polycystic-ovary-syndrome - gene-expression - biomedical literature - knowledge discovery - text - identification - information - patterns - microarrays - hypotheses
Abstract Background: High throughput microarray analyses result in many differentially expressed genes that are potentially responsible for the biological process of interest. In order to identify biological similarities between genes, publications from MEDLINE were identified in which pairs of gene names and combinations of gene name with specific keywords were co-mentioned. Results: MEDLINE search strings for 15,621 known genes and 3,731 keywords were generated and validated. PubMed IDs were retrieved from MEDLINE and relative probability of cooccurrences of all gene-gene and gene-keyword pairs determined. To assess gene clustering according to literature co-publication, 150 genes consisting of 8 sets with known connections (same pathway, same protein complex, or same cellular localization, etc.) were run through the program. Receiver operator characteristics (ROC) analyses showed that most gene sets were clustered much better than expected by random chance. To test grouping of genes from real microarray data, 221 differentially expressed genes from a microarray experiment were analyzed with CoPub Mapper, which resulted in several relevant clusters of genes with biological process and disease keywords. In addition, all genes versus keywords were hierarchical clustered to reveal a complete grouping of published genes based on co-occurrence. Conclusion: The CoPub Mapper program allows for quick and versatile querying of co-published genes and keywords and can be successfully used to cluster predefined groups of genes and microarray data
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