Published online 8 May 2009 Nucleic Acids Research, 2009, Vol. 37, Web Server issue W141–W146 doi:10.1093/nar/gkp353 MedlineRanker: flexible ranking of biomedical literature Jean-Fred Fontaine*, Adriano Barbosa-Silva, Martin Schaefer, Matthew R. Huska, Enrique M. Muro and Miguel A. Andrade-Navarro Computational Biology and Data Mining Group, Max Delbru¨ ck Center for Molecular Medicine, Robert-Ro¨ ssle-Strasse. 10, D-13125, Berlin, Germany Received January 31, 2009; Revised April 21, 2009; Accepted April 22, 2009 ABSTRACT The biomedical literature is represented by millions of abstracts available in the Medline data- base. These abstracts can be queried with the PubMed interface, which provides a keyword- based Boolean search engine. This approach shows limitations in the retrieval of abstracts related to very specific topics, as it is difficult for a non- expert user to find all of the most relevant keywords related to a biomedical topic. Additionally, when searching for more general topics, the same approach may return hundreds of unranked refer- ences. To address these issues, text mining tools have been developed to help scientists focus on relevant abstracts. We have implemented the MedlineRanker webserver, which allows a flexible ranking of Medline for a topic of interest without expert knowledge. Given some abstracts related to a topic, the program deduces automatically the most discriminative words in comparison to a random selection. These words are used to score other abstracts, including those from not yet anno- tated recent publications, which can be then ranked by relevance. We show that our tool can be highly accurate and that it is able to process millions of abstracts in a practical amount of time. MedlineRanker is free for use and is available at http://cbdm.mdc-berlin.de/tools/medlineranker. INTRODUCTION Millions of abstracts from biomedical articles are avail- able in the Medline database. Its PubMed query interface, which uses keywords to retrieve related records, returns a list of abstracts, which are not sorted by relevance. If a search for a general topic is performed, hundreds or thousands of records may be returned; in this case, a user is likely to check only the top of the list. Thus, interesting abstracts may be hidden to the user because of their random position in the list of results. Furthermore, for a very speciﬁc biological ﬁeld, non-expert users would not be able to provide all the relevant keywords for the query. To improve text retrieval by scientists, text mining tools have been developed that oﬀer alternative ways to query and select abstracts from the Medline database. By computationally preprocessing the Medline records, it is possible to focus on speciﬁc topics and ﬁlter out abstracts that are not relevant to the topic of interest. For this purpose, abstract annotations by the Medical Subject Headings (MeSH) thesaurus and the Gene Ontology terms can be used to cluster the Medline records like in the XplorMed (1), the GOPubMed (2) or the McSyBi (3) tools. By using text extraction methods, some tools apply a co-occurrence analysis at the sentence level to predict relationships between genes, proteins or input keywords like EBIMed (4) or ReleMed (5). EBIMed focuses on abstracts describing protein–protein interactions (PPI), and ReleMed analyses the co-occur- rence of a set of input keywords in the same sentence. These tools are useful for managing the results from a PubMed query. However, a proper set of keywords are still required to query the database, and these may not be obvious for a non-expert user. Making a query to Medline without using keywords or without knowing a speciﬁc vocabulary or query lan- guage is also possible using various text mining methods. A plain language sentence can be translated into the proper keyword-based query language of PubMed using the askMEDLINE tool (6). Alternatively, one abstract or text paragraph can be used as a model to ﬁnd similar records using the PubMed related article feature (7) or the eTBLAST tool (8). Abstracts sharing similar annota- tions or words are likely to be related to the input. Yet, one single abstract or text paragraph may not be the best sample for a whole biomedical ﬁeld and the resulting list is expected to contain irrelevant abstracts. Few methods have proposed automatic extraction of relevant information from a set of abstracts representing a topic of interest, and the use of this information to *To whom correspondence should be addressed. Tel: +49 30 9406 4307; Fax: +49 30 9406 4240; Email: jean-fred.fontaine@mdc-berlin.de 2009 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.