CoPub Mapper: mining MEDLINE based on search term co-publication
© Alako et al; licensee BioMed Central Ltd. 2005
Received: 21 December 2004
Accepted: 11 March 2005
Published: 11 March 2005
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.
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 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.
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.
High throughput microarray analysis has made it possible to analyze the mRNA expression of most if not all human genes simultaneously [1, 2]. The data generated from these analyses are overwhelming since hundreds of interesting differentially expressed genes can be identified in a single assay. Knowledge on expression levels of genes in different systems is useful, but does not directly answer biologically relevant questions, such as: What is the gene function? Where is the gene located within the genome? Where is the protein located within the cell? Most important is the answer to the question whether genes identified in microarray experiments have something in common, such as, are multiple genes part of a single biological pathway or proteins part of a protein complex? The public database which contains much of the relevant information to answer these questions is MEDLINE. Therefore, mining the MEDLINE database for all information on a set of genes of interest to extract and evaluate their co-occurrences with biological keywords and other genes, could reveal biologically relevant pathways [3–6].
The most widely used methodology to identify genes and proteins in text is by thesaurus-based concept extraction. Using a predefined gene name list, text phrases are compared to the thesaurus for matching. Complications for gene name thesauri are variations in full name spelling, use of abbreviations (gene symbols), the large number of synonyms (different name but same gene) and homonyms (same name but meaning different genes or unrelated concepts) [7, 8]. Particularly homonyms in the form of abbreviations and acronyms create a serious problem of false positive assignment of a gene to a particular concept [9–13]. A complementary approach for gene/protein identification is "named entity recognition" in which a program learns to recognize concepts from text [14–16]. Due to the enormous synonym and homonym problems, named entity recognition encounters difficulties in achieving high performance gene name identification. A next step in text mining is linking of different concepts (such as gene names and keywords) that are identified. In the simplest method, co-occurrence of two concepts within the document can be used as an indication of linkage. Extensions of co-occurrence can include (i) the number of times a concept is found, (ii) how close concepts are to one another, such as, within a single sentence, and (iii) not just two, but the weighed combination of all concepts within a document. More sophisticated fact extraction methods can also retrieve information on the type of relationship between two concepts. Natural language processing (NLP) grammatically parses whole sentences to identify verbs and other connecting phrases that describe the correlation between concepts [3, 4, 6, 17]. A third step in text mining takes linked concepts and groups them according to their co-occurrence and relationships. Again, this can be performed by simple clustering of the co-occurrence of pairs of concepts as well as complex multi-dimensional classification using weighed concept combinations [18, 19]. This type of clustering of, for example, differentially expressed genes from a microarray experiment, can disclose, summarize, and visualize published knowledge, but can also be utilized for novel information discovery [5, 20]. Although progress is being made in higher order literature processing, text mining applications in the field of genomics are mainly thesaurus and co-occurrence based. Such programs and methods to identify potential functional correlations between genes have been described [21–33]. Each of these applications has its unique advantages and limitations, showing the broad range of needs for text mining as well as the numerous extraction, linking, and discovery methods feasible.
We set out to create a well annotated and curated open source gene list including full names, symbols and aliases and a regular expression-based search method to identify genes in text databases such as MEDLINE. In addition to the gene thesaurus, specific keyword lists were generated for co-occurrence analyses. For each concept, PubMed identifiers (IDs) from MEDLINE documents containing the concept were extracted, all gene-gene and gene-keyword co-occurrence pairs identified and stored in a database for fast co-occurrence retrieval. This database can be mined using single or batches of concepts to retrieve co-occurrences that form the input in clustering programs to group genes and keywords according to their similarity in co-publications. The program, database and all thesauri are freely available and can be adapted to include updates, new thesauri, and search methods.
Human gene thesaurus
CoPub Mapper gene and keyword database information. Gene names, symbols and aliases were retrieved from Affymetrix HG_U95 / HG_U133  and the HUGO databases . The keyword thesauri include the three Gene Ontology subsections , diseases  and tissues/organs .
Number of terms
Number of terms with MEDLINE hits
Total number of MEDLINE citations
Affymetrix HG_U95-133 HUGO
National Library of Medicine
Semi-automatic stemming was performed on "gene-level name" and "gene-level additional description" fields by removing numbers, letters, and phrases like "alpha", "member", "type", "class", etc. This resulted in a stem-level gene name description. Although the current version of CoPub Mapper does not take this stem-level into account, these fields are part of the gene thesaurus and freely available.
In total, five different keyword thesauri were compiled including the Gene Ontology "biological process", "cellular component", and "molecular function", as well as "diseases" and "tissues" (Table 1). In the disease thesaurus, commas were replaced with the Boolean "OR" operator. All keyword databases were manually curated to remove terms too specific or too common.
MEDLINE concept extraction and curation
The full MEDLINE baseline XML files (until January 2004) were obtained from the National Library of Medicine , extracted to small text files containing title, abstract and substances using BioPerl API. The title, substance and abstract fields from MEDLINE records from 1966 to January 2004 were searched for the presence of different case-insensitive gene and keyword concepts using Perl compatible regular expressions (PCRE). For the gene-level name descriptions the characters "] [.-)(,:;" and space were allowed preceding and following the gene-level name description and also an optional "s" was permitted to follow the name. Any space in the gene-level name description was allowed to be a space or a dash. The same regular expressions were applied to the gene name stem-level descriptions, except that, the description could also be followed by any single letter or a number between 0 and 99. Gene symbols and aliases could be preceded and followed by the characters "] [.-)(,:;" and space. After the first two characters, the presence of a dash was allowed in between the characters of the symbols and aliases (to take, for example, both "bcl2" and "bcl-2" into account). The concepts of the keyword files could be preceded and followed by the characters "][.-)(,:;" and space. In addition, "s" and "'s" were allowed to follow the disease concept. As for the gene-level name descriptions, a dash was allowed to be present between the words of a keyword concept. Per annotated gene or keyword, the PubMed IDs of MEDLINE records in which the concept was identified were stored in a MySQL database.
In order to identify potential problem concepts, 50 genes and 50 keywords with the highest number of PubMed IDs were manually inspected and curated if appropriate. In addition, a random selection of genes and all keywords that gave less than 2 MEDLINE hits were examined and this evaluation was used to optimise the thesauri and regular expressions search strategy described above.
To address the homonym issue, a correction was made for possible discrepancies between a parenthesised gene symbol and its expected name. All abbreviations in parenthesis in MEDLINE abstracts were retrieved in combination with 4 preceding words. In total, 1,105,669 MEDLINE records were identified where the abbreviation matched a gene symbol or alias. For all these records, 4 words preceding the abbreviation were compared to the gene-level name description of that particular gene. If none of the words resembled partly the gene name, the PubMed ID was removed from that particular gene's PubMed ID list. Using this method, 603,580 records were deleted from the gene hit database resolving part of the gene-unrelated concept homonym problems. Manual inspection of 173 random records revealed that, extrapolated, 79 % of the 603,580 records was correctly removed, while 7 % of the 502,089 non-removed records should have been deleted.
In our examination of genes with the highest number of PubMed IDs and our first CoPub Mapper analyses, we noticed a distinct contamination of records identifying gene symbols and aliases by abbreviation used for cell lines (such as PC3 which is an alias for 3 genes as well as a prostate cancer cell line). Since full names of cell line abbreviations are rarely put in writing, the homonym correction did not eliminate these discrepancies. A list of cell line names was retrieved  and gene symbols and aliases that fitted a cell line name were further processed. From 106 genes that included one of the cell line homonym names, all MEDLINE records were deleted in which the cell line name was mentioned without the presence of the stem-level gene name. In total, 100,213 PubMed IDs were eliminated. A manual inspection of 78 randomly chosen records showed that 87 % were correctly removed.
Database set-up and CoPub Mapper program
A file was generated that contains a unique query ID and the probeset IDs, UniGene (combination of Aug 2002 and Oct 2003 builds) and RefSeq identifiers for each of the individual 15,621 entries in the gene thesaurus (alias_affygene). In addition, a file with the gene name, symbol and aliases and unique query ID was created (query_affygene).
The retrieved PubMed IDs from each field (gene names, symbols and aliases) of the 15,621 unique gene thesaurus query IDs were non-redundantly combined into a MySQL database (lit_affygene) and a separate data-file (litstat_affygene) in which the number of PubMed IDs per query was counted. Furthermore, the PubMed IDs from the keyword thesauri were per concept stored (query_keyword, lit-keyword and litstat_keyword). Per gene-gene pair and gene-keyword pair, overlaps in PubMed IDs were identified and separately stored in the database (pair_keyword _affygene). From these paired files, a pairstat file was generated containing the number of PubMed IDs of each concept, the number of overlapping PubMed IDs between the two concepts and a relative score. The relative score is based on the mutual information measure and was calculated as
S = PAB/PA * PB
in which PA is the number of hits for concept A divided by the total number of PubMed IDs, PB is the number of hits for concept B divided by the total number of PubMed IDs, and PAB is the number of co-occurrences between concepts A and B divided by the total number of PubMed IDs. The relative score is produced as a log10 conversion and in the batch search option in a 1–100 scaled log10 conversion:
R = 10log S
and the scaled log transformed relative score:
R' = 1 + 99 * (R - Rmin) / (Rmax - Rmin)
where Rmin and Rmax are the lowest and highest R values in each pairstat file, respectively.
The CoPub program was generated in Python and runs as a web-based application (CGI script). The text output of a batch search can be saved and imported into a clustering program such as Cluster  and SpotFire (Spotfire, Göteborg, Sweden). The HTML output of "number of hits", "relative score", and batch search results are hyperlinked to the MEDLINE database at the European Bioinformatics Institute  for direct manuscript retrieval.
Performance evaluation using ROC (receiver operating characteristics) curves
CoPub Mapper test groups. Eight groups of genes with a common function, process, cellular location, or microarray expression profile, were defined from gene ontology (GO), BioCarta, or a microarray experiment. The genes used for CoPub Mapper analysis were randomly selected from larger sets of genes part of the 8 different groups.
smooth muscle contraction
GO (Biological Process)
GO (Molecular Function)
GO (Cellular Component)
GO (Cellular Component)
GO (Molecular Function)
GO (Biological Process)
UniGEM V microarray: stroma vs epithelial cells
For a systematic evaluation of performance we applied Receiver Operating Characteristics (ROC) graphs and the area under the ROC curve (AUC) as an outcome measure. To use this method all genes from the 8 subgroups are pooled into one set. To calculate an AUC for every gene we used the following procedure. A gene from the pooled set is selected as a seed. The seed is paired with all other genes in the set and non-centered Pearson correlation coefficients are calculated based on their co-occurrence profiles. The co-occurrence profile is one row of the co-occurrence matrix under investigation. The genes are ordered by their correlation coefficients, with the highest value at the first rank. To generate a ROC curve, the obtained ranking of the genes is viewed as the outcome of a classifier. For a seed, genes from the same subgroup are called positives and all other genes are called negatives. ROC curves are two-dimensional graphs in which the true-positive (TP) rate is plotted against the false-positive (FP) rate. The TP rate is defined as correctly classified positives divided by all positives. The FP rate is defined as incorrectly classified negatives divided by all negatives. While running down the list, for every rank the true and false positive rate are calculated, by taking all encountered genes to be classified as positive and all not yet encountered genes as negative. The AUC of the ROC curve is calculated. The procedure is repeated until an AUC has been calculated for every gene in the pooled set. An average AUC is calculated per subgroup. The AUC measure varies between 0 and 1. Random ordering gives an AUC of 0.5 and an AUC of 1 represents perfect ordering, i.e. all positives are at the top of the list with no negatives in between, indicating perfect co-occurrence clustering of the genes in the subgroup .
Validation of CoPub Mapper co-occurrence profiling
Microarray analysis using CoPub Mapper
In order to validate the CoPub Mapper program with real microarray data, a set of differentially expressed genes was selected from a comparison between ovaries of healthy women and women suffering from Poly Cystic Ovary Syndrome (PCOS) . PCOS is characterized by a combination of chronic anovulation, hyperandrogenism and cysts in ovaries and is the most common cause of anovulatory infertility. Also hyperinsulinemia and obesity can be observed in many PCOS patients [46, 47].
Single Gene-Keyword extraction
CoPub Mapper single gene pair output. Output of the "Single Gene Pair Mapper" in which the top ten genes co-published with the androgen receptor are listed according to number of co-publications (Pmid hits).
kallikrein 3, prostate specific antigen
nuclear receptor subfamily 3, group C, member 1; glucocorticoid receptor
aminopeptidase puromycin sensitive
sex hormone-binding globulin
gonadotropin-releasing hormone 1, leutinizing-releasing hormone
GNRH, GRH, LHRH, LNRH
epidermal growth factor, beta-urogastrone
tumor protein p53
CoPub Mapper single gene biological concept output. Output of the "Single Gene Biological Term Mapper" in which the top ten diseases co-published with the androgen receptor are listed according to their relevance score.
Number of hits
log10 Relative Score
Muscular Atrophy Spinal
Sex Chromosome Aberrations
X-Linked Myotubular Myopathy
CoPub Mapper single gene biological concept output. Output of the "Single Gene Biological Term Mapper" in which the top ten genes co-published with the prostate cancer disease-keyword are listed according to number of co-publications.
Number of hits
log10 Relative Score
kallikrein 3, prostate specific antigen
aminopeptidase puromycin sensitive
androgen receptor, dihydrotestosterone receptor
acid phosphatase, prostate
GNRH, GRH, LHRH, LNRH
1, leutinizing-releasing hormone tumor protein p53
B-cell CLL/lymphoma 2
epidermal growth factor, beta- urogastrone
cyclin-dependent kinase inhibitor 1A
CAP20, CDKN1, CIP1, MDA-6, P21, SDI1, WAF1
Meta-analysis: all genes versus keywords
With the implementation of high-throughput technologies in many fields of research, problems have shifted from data gathering to data comprehension. Linking data from different sources, such as microarray expression data to biomedical text corpora, can assist in the disclosure, summary, and visualisation of knowledge. This is particularly valuable when from high throughput data, only a few items can be selected for further detailed low-throughput examination. Co-occurrence analysis of concepts using the MEDLINE literature database, is an effective tool to extract and categorize published knowledge. CoPub Mapper output was successfully used to cluster predefined groups of genes and resulted in a commonsensical clustering of PCOS microarray data. In addition, CoPub Mapper uncovered relationships between genes using single concept searches and provided an overall gene-keyword clustered summary of the literature. One obvious limitation of gene-driven text mining is the incomplete study and publication of all human genes. Out of approximately 30,000 human genes, we included 15,621 annotated genes of which 10,700 were mentioned at least once and 9,769 at least twice in MEDLINE. The use of human gene names, symbols and aliases does not necessarily mean a human-specific literature search. Many gene names and symbols are shared by other species as well.
The main advantages of CoPub Mapper above most other co-publication programs, are its modularity of keyword databases and the pre-calculated co-occurrences. Based on the results from the predefined groups of genes, the choice of keyword database made a substantial difference in clustering efficiency as determined by AUC calculations. Utilisation of a single joint thesaurus could counteract clustering due to inclusion of irrelevant non-discriminating keywords. Another illustration that keyword selection is an important issue, is the prevalence of common keywords such as "cancer" (disease), "membrane" (cellular component), "metabolism" (biological process), "receptor" (molecular function), and "blood" (tissue). These keywords are co-published with nearly any gene of interest and were identified using CoPub Mapper. Although the relative score is generally low, these co-occurrences will influence the clustering process. Manual removal or stringent selection criteria before clustering can largely eliminate this potential bias. Addition of new keyword thesauri such as species, technologies, drugs, toxicology, pathology, etc. is feasible. Pre-calculation of co-publication of all possible gene-gene and gene-keyword pairs and storage in the pairstat data file, makes querying the database extremely efficient. Although the data are present, CoPub Mapper is not programmed for co-occurrence querying of more than 2 concepts. We are currently integrating CoPub Mapper into the Sequence Retrieval System (SRS) for multi-concept interrogation and direct linkage to other databases (such as microarray data, Gene Ontology, OMIM, SwissProt, LocusLink, UniGene, Ensembl, etc.) .
Comparing the gene expression profiles of normal versus PCOS ovaries has identified a large number of genes representing networks and pathways that are deregulated in PCOS. However, the gene names and symbols hardly ever point to specific signal transduction pathways. The relation of genes with their function, localization and context has been described in literature. Here we show that within the list of differentially expressed genes some are linked to PCOS, obesity, diabetes and gametogenesis. This is without surprise and easily explained [46, 47]. Other genes are linked to cell proliferation, differentiation and cancer. Most of them were downregulated which correlates with the observed arrest in growth and differentiation of follicles. Other clusters with no obvious link to PCOS may shed new light on the genes and pathways involved in the disease.
One of the major challenges associated with compiled heterogeneous text records such as MEDLINE, is correct gene recognition and assignment. The lack of consistent gene naming has resulted in a flood of synonyms and homonyms . Although the synonym issue can be resolved by accumulating all different gene names and symbols, the correction for homonyms is still a daunting task. In order to include different spelling forms and the word context, we performed the text searches case insensitive and with predefined rules of regular expression.
The homonym problem consists of (i) different genes with identical gene name, symbol, or alias, and (ii), more frequently, a gene name, symbol or alias used for other terms than genes . In the curated CoPub Mapper gene thesaurus, 1,286 of the 15,621 annotated genes (8.2 %) share a symbol or alias. In order to limit both aspects of the homonym problem, we (i) eliminated 2 letter symbols and aliases, (ii) deleted all symbols and aliases present in the English dictionary, (iii) manually curated terms with exceptionally high number of hits, (iv) corrected for cell line names, and (v) deleted records in which the preceding description of parenthesised symbols or aliases did not match the corresponding gene name. This last method has been used before to make an inventory of the homonym problem and provide strategies for correction, such as the one used here [9–13]. Although these measures effectively reduced the homonym problem, one will regularly encounter incorrect record assignment and invalid co-occurrence quotation using CoPub Mapper. Additional optimisation of the gene thesaurus might further reduce this problem to some extent, but other correction approaches should be considered. One of the most promising strategies to achieve disambiguation is based on the preferential co-occurrence of other concepts [9, 10]. For example, concepts generally co-published with PSA meaning Poultry Science Association, will be very different from concepts co-published with PSA representing prostate specific antigen. Based on these preferential co-occurring concepts, one can assign the correct meaning to an ambiguous term.
Besides disclosure, summary, and visualisation of known facts using co-publication, one could also discover novel linkages among genes and between genes and other concepts. One possibility to identify unpublished, but plausible links, is to screen for black squares surrounded by red ones in a clustered co-occurrence heat map as shown in Figure 5. The fact that a particular gene-disease combination was not found in MEDLINE (black square), but clustered together with other co-published gene-disease pairs (red squares), could indicate an unpublished association. This approach shows analogies with the Swanson discovery framework in which concept A is known to relate to B and B is associated with C [49, 50]. Combining all data, the deduction that A relates to C can be hypothesised and tested [49, 51–53].
CoPub Mapper is a program that identifies and rates co-published genes and keywords starting from a single concept search or batch-wise from a set of genes. Its modularity and pre-calculated co-occurrences allow for quick and versatile querying. The regular-expression search strategy and homonym correction makes the keyword database comprehensive and less contaminated with false positive classifications. CoPub Mapper can be used to summarize, evaluate and categorise annotated genes from microarray analyses based on co-occurrences with biological keywords and other published genes.
Availability and requirements
We thank Edwin van den Heuvel, Victor de Jager, Rene van Schaik, Jacob de Vlieg, and BioASP for their support, NLM (National Library of Medicine) for licensing of MEDLINE and Jan Kors and Jeannette Kluess for careful reading of the manuscript.
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