Background

Collaboration among investigators and research groups in the biomedical field has become increasingly crucial to achieving success in the understanding of complex diseases such as cancer and heart disease [1]. As a result, many networks and consortia have been established to promote collaboration and data sharing. Networking of investigators and searching for potential collaborators in a specific research domain will be especially important in the genomics era, which provides an opportunity to apply basic research to the promotion of human health and disease prevention. The HuGENet initiative to develop a "network of investigator networks" in human genome epidemiology [2] illustrates the efforts of a diverse, global research community that is committed to accelerating the development and synthesis of knowledge on genetic variation and human diseases [3]. As more researchers recognize the importance of establishing networks to enhance efficiency and reduce redundancy in scientific research, major challenges include identifying investigators with particular interests and acquiring contact information for building new networks and updating this information for existing networks.

PubMed [4], offering access to the MEDLINE database of citations and abstracts of biomedical research articles, provides one of the most valuable information resources for tracking the progress of biomedical research through the published literature; it can also be used to find collaborators and investigators by authorship. Citation analyses that address the structure of scientific collaboration networks have been done many times [5–8]. Our approach shows how information contained in PubMed abstracts and author affiliation strings can be used to extend existing networks even further by identifying more investigators who may be new collaborators. In this paper, we present a novel PubMed-based approach to building a dynamic investigator network with detailed investigator profiles that include institutional affiliation, country of origin, email address, and publication history. We illustrate our concept using a prototypical web-based system for building an investigator network.

Methods

Data sources

We used 20,000 randomly selected PubMed abstracts from articles published between 2001 and 2005 (PubMed data) to determine the extent of affiliation data in PubMed. We used a continuously updated literature database of studies relevant to human genome epidemiology (HuGE Pub Lit [9]) to create a prototype web-based system for building an investigator network. As of October 19, 2006, the HuGE Pub Lit database contained 23,876 PubMed abstracts of gene-disease association studies (HuGE PubMed data).

The National Center for Biotechnical Information Entrez Programming Utilities (NCBI E-utility) [10] was used to retrieve full PubMed records containing title, authors, abstract, and affiliations based on PubMed Unique Identifier (PMID). We took advantage of the fact that most PubMed abstracts are indexed with National Library of Medicine medical subject headings (MeSH) terms by NCBI staff. We used a standard vocabulary, Unified Medical Language System (UMLS) metathesaurus (version 2006AB) [11], to index PubMed abstracts by converting MeSH terms to UMLS concept unique identifiers (CUIs). To enrich the capacity of UMLS to handle gene information, we incorporated Entrez gene records into the UMLS metathesaurus, substituting Entrez gene IDs for the UMLS CUIs. Gene symbols were indexed manually using these Entrez gene IDs [12]. The MeSH hierarchy tree [13] was used to provide "children" concepts for query terms.

Affiliation parsing

PubMed affiliation string format

While building the affiliation parsing tool, we found that over 80% of the affiliation strings in PubMed articles adhered to the following format:

[address component], [address component], ..., [country]. [email].

Country name lookup list

We created a country lookup table containing country names and their synonyms based on International Organization for Standardization 3166 country codes [14] and UMLS. The UMLS metathesaurus lists numerous synonyms for country names, for example, United States, US, U.S.A., etc. Using this table, country names could be assigned to 86% of the affiliation strings. The remaining affiliation strings could not be parsed for one or more of the following reasons: 1) a noncountry geographic location, such as a city or state, was provided instead of a country name; 2) the affiliation was written in a language other than English; or 3) the affiliation was provided in an unconventional format. To handle the first two scenarios, we created a custom country name list by manually inspecting these affiliation strings and adding the geographic locations as synonyms for countries. For example, if "Beijing" was in an affiliation string without country information, we added "Beijing" to the lookup table as a synonym for China in the custom country name list. We used a second-run parsing algorithm if the affiliation was provided in unconventional format.

Email address parsing pattern

A regular expression pattern was used to find and parse the email address in the affiliation string (see detail in the appendix file)

Institution key work list

To capture this information, including some in languages other than English, we created an institution key word list (Table 1).

Table 1 Keyword list for parsing institution information
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Detailed affiliation parsing algorithm can be found in the appendix file.

Example of parsed affiliation

Original affiliation string: Pulmonary and Critical Care Medicine, Yale University School of Medicine, 300 Cedar Street, TAC-441S, PO Box 208057, New Haven, CT 06520, USA. geoffrey.chupp@yale.edu.

Parsed information:

Full address: Pulmonary and Critical Care Medicine, Yale University School of Medicine, 300 Cedar Street, TAC-441S, PO Box 208057, New Haven, CT 06520, USA

Country: USA (CUI code: C0041703)

Institution: Yale University School of Medicine

Email: geoffrey.chupp@yale.edu

Web-based demonstration version of the system implementing the methodology

We generated a relational database that linked PubMed abstract content, detailed investigator profiles, and indexed UMLS/Entrez gene concepts. Because PubMed abstracts provide an affiliation only for the first author, the parsed affiliation information was linked to the first author of the corresponding publication abstract. A diagram of the database schema is shown in Figure 1.

Figure 1
figure 1

Relational database schema. Note: UMLS – Unified Medical Language System. CUI – Concept Unique Identifier. MeSH – Medical Subject Heading. PK – Primary Key. FK – Foreign Key

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Java J2EE 1.4 [15] was used to build the web-based system combined with the open-source frameworks Hibernate [16] and Struts [17]. The Microsoft SQL server was used as the back-end database.

Performance Evaluation

Two test sets were used to assess the accuracy of the parsing application. We extracted all 311 records (HuGE PubMed test data) added to HuGE Pub Lit between October 20, 2006, and November 3, 2006, and randomly selected 311 articles (PubMed test data) that had been added to the PubMed database during the same period

By using preterm birth as a test case, we tested the system's ability to dynamically create domain-specific investigator networks. After consulting with an expert in the domain of preterm birth, the following query was used to search the database: "prematurity or infant, premature or infant, low birth weight or labor, premature." We compared the members of the dynamic investigator network built by using our system with the membership of an existing network, the International PRE term BI rth C ollaborative (PREBIC), which includes a subgroup for study of genetic factors in preterm birth [18].

To further evaluate the performance of the methodology, we invited domain experts in the fields of human genome epidemiology of preterm birth, Chlamydia infection and HIV infection to participate in the tests. The experts performed the search using the Investigator Browser by choosing their own search terms. Each expert reviewed the list of investigators generated by the Investigator Browser and labeled the ones they had collaborated with or recognized as investigators in their field; they also provided us with investigator names that they expected to find but that were not on the list. We used this information to estimate sensitivity of the methodology.

Results

Extent of affiliation information in general PubMed abstracts and HuGE PubMed abstracts

In our sample of general PubMed abstracts, 87% had affiliation strings; those lacking them were mostly nonresearch publications such as biographies, comments, or letters. In all, 98.6% of HuGE PubMed abstracts contained affiliation strings. Email information was available in about 40% of both general PubMed records and HuGE PubMed records. In both datasets, affiliation profiles could be constructed for about 20% of all authors (Table 2).

Table 2 Affiliation information available from records in PubMed and HuGE Pub Lit
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Performance Evaluation

Our parsing tool was able to obtain all email addresses in the valid format by using regular expression pattern matching (see Methods). Performance of affiliation parsing is given in Table 3.

Table 3 Affiliation parsing performance
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Comparing the list of investigators generated by the methodology with information provided by domain experts showed that our approach could identify about 70% – 85% of investigators in three different research areas with the selection of the first or last authors only while over 90% of investigators were identified if all authorship was considered (Table 4).

Table 4 Comparison of investigators identified by experts and the methodology
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By using a domain-specific query (see Methods) and the web-based prototype system, we dynamically built an investigator network for the HuGE field focused on genetic factors in preterm birth. The HuGE Pub Lit database contained 122 relevant abstracts, from which we identified 548 investigators (authors), including 178 who were represented as either first or last authors. Detailed profiles for each investigator included the number of publications in PubMed, number of publications in HuGE Pub Lit, and number of HuGE publications as the first or last author. Of the 10 genetics investigators within the PREBIC network, 9 were included in the list of investigators returned by web-based network building system. One investigator was missed because he had not yet published any articles that were included in HuGE Pub Lit.

Web-based demonstration version of the system

With this system, we were able to retrieve articles using a query for a specific domain of interest identified by indexed UMLS terms, all possible children terms, and text word searching of title and abstract to generate a dynamic, user-defined network with a list of authors and detailed author profiles. This approach allows users to construct domain-specific investigator networks (Figure 1); browse investigators and corresponding investigator profiles (Figure 2); and stratify the investigators by country (Figure 3) and institution (Figure 4).

Figure 2
figure 2

Results of Investigator Browser search for HIV investigator network in human genome epidemiology.

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Figure 3
figure 3

Investigator Browser showing an investigator detail profile in HIV investigator network in human genome epidemiology.

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Figure 4
figure 4

Investigator Browser presentation of country distribution in HIV investigator network in human genome epidemiology.

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The demonstration version of the system implementing this methodology can be accessed [25].

Discussion

Investigator networking and collaboration is common practice in modern scientific research, aided by the emergence of new technology, especially the Internet. Collaboration can greatly enhance research by increasing the volume of high-quality data available to investigators and accelerating progress toward research goals [19, 20]. The HuGENet movement [2] has made great efforts to promote global collaboration among investigators conducting population-based research in genetic epidemiology. Recently, HuGENet launched an initiative to establish a "network of networks" across the field by registering existing networks, teams, and investigators to share data, develop standards, facilitate the confirmation of research findings, and reduce duplication of effort [1, 21]. Domain-specific investigator networks created by our prototype system could be instrumental in identifying additional investigators to recruit to these networks.

Citation analysis of the published literature is a reliable method for describing scientific collaboration networks by identifying and connecting authors that have made contributions in the same research field [7]. MEDLINE is the largest component of PubMed, the freely accessible online database of biomedical journal citations and abstracts created by the U.S. National Library of Medicine (NLM). With the assistance of information technology, PubMed allows for quick elucidation of comprehensive investigator networks. In addition to abstract content and author names, PubMed provides limited affiliation information (including country, institution, and contact information), which has practical value for network building. The ability to search MeSH-indexed abstracts allows domain-specific investigator networks to be generated dynamically. Quick and up-to-date answers to the "3W" questions (Who, Where, and What) can be obtained without soliciting investigators.

Affiliation strings in PubMed records have been used to analyze the geographic distribution of published studies [22, 23]. However, the heterogeneity of country names has required time-consuming manual extraction procedures that precluded the generation of large datasets. We successfully developed and implemented an automated approach that uses the UMLS to accurately and robustly parse the affiliation string. Our affiliation parsing strategy demonstrates the capacity to extract investigator profile information efficiently from PubMed records.

Although our approach provides a new way to explore and build investigator networks from PubMed, it has many limitations. First, PubMed records identify authors only by last name and first initial, which can create some ambiguity in investigator networks generated by our system. However, this may not be a consideration in the future, because PubMed recently started to provide full names in XML format. Second, because PubMed provides affiliation information only for the first author, detailed investigator profiles can be generated only for investigators with publications in which they are first author. Third, indexing of institutions could not be completely automated because of inconsistency in the institution names provided by authors, a problem that could be addressed by establishing an international registry of research institutions. Finally, PubMed does not include all biomedical journals, especially those published in other countries. Adapting the current system for other data sources such as EMBASE [24] could result in more comprehensive, dynamically created investigator networks.

Conclusion

The new approach presented in this paper uses information available in PubMed abstracts as an efficient way to identify potential collaborators in a particular research domain. We demonstrated this approach in the field of human genome epidemiology, but it could be applied to any field represented in PubMed to track investigators and dynamically create domain-specific investigator networks.