CoPub Mapper: mining MEDLINE based on search term co-publication

By Blaise T. F. Alako, A. Veldhoven, S. V. Baal, Rob Jelier, S. Verhoeven, T. Rullmann, J. Polman, G. Jenster. 2005

82 citations recorded by the source corpus.

BackgroundHigh 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.ResultsMEDLINE 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 co-occurrences 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.ConclusionThe 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.

Subfields: Molecular Biology

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In Molecular Biology

Citations in the corpus

Natural Language Processing for EHR-Based Computational Phenotyping (2018, neutral): “CoPub Mapper [21] ranks co-occurrence associations between genes and biological terms from PubMed. iHOP [22] links interacting proteins to their corresponding databases and uses co-occurrence information to build a graphical interaction network.”
DISEASES: Text mining and data integration of disease–gene associations (2014, background): “Disease–gene associations extracted from Medline abstracts can already be searched through generalized co-occurrence tools such as CoPub [20,24] and FACTA+ [22,25].”