eTBLAST: a web server to identify expert reviewers, appropriate journals and similar publications

Abstract

Authors, editors and reviewers alike use the biomedical literature to identify appropriate journals in which to publish, potential reviewers for papers or grants, and collaborators (or competitors) with similar interests. Traditionally, this process has either relied upon personal expertise and knowledge or upon a somewhat unsystematic and laborious process of manually searching through the literature for trends. To help with these tasks, we report three utilities that parse and summarize the results of an abstract similarity search to find appropriate journals for publication, authors with expertise in a given field, and documents similar to a submitted query. The utilities are based upon a program, eTBLAST, designed to identify similar documents within literature databases such as (but not limited to) MEDLINE. These services are freely accessible through the Internet at http://invention.swmed.edu/etblast/etblast.shtml, where users can upload a file or paste text such as an abstract into the browser interface.

Figure 1.

Example output for similarity search (A), experts (B), journals (C) and history of publications (D) obtained with the abstract from PMID16179260 (11) as an illustrative query. The highly similar match flag is raised for the paper from which the abstract was obtained. The top expert is the leading author in the paper from which the abstract was obtained. Finally the first suggested journal is Cell, in which this article was published. These results are biased, since the original paper was not removed from the results. This was done on purpose to illustrate the usage of eTBLAST server.

Figure 2.

Expertise scores can be used to clearly identify a threshold enabling identification of ‘true’ experts. A synthetic set of a 1000 abstracts (see text, e.g. non-sensical queries that resemble a typical abstract) was used to determine the score distribution for authors (experts) found in the most similar articles returned by eTBLAST. A second set of 1000 abstracts from randomly selected articles in Medline was used to obtain the score distribution of the first, second, third and tenth authors (experts) on the Find an Expert output list. As the rank of the experts increases, the distribution tends to shift toward the left, with lower scores.