Background: Advances in biotechnology and in high-throughput methods for gene analysis have contributed to an exponential increase in the number of scientific publications in these fields of study. While much of the data and results described in these articles are entered and annotated in the various existing biomedical databases, the scientific literature is still the major source of information. There is, therefore, a growing need for text mining and information retrieval tools to help researchers find the relevant articles for their study. To tackle this, several tools have been proposed to provide alternative solutions for specific user requests. Results: This paper presents QuExT, a new PubMed-based document retrieval and prioritization tool that, from a given list of genes, searches for the most relevant results from the literature. QuExT follows a concept-oriented query expansion methodology to find documents containing concepts related to the genes in the user input, such as protein and pathway names. The retrieved documents are ranked according to user-definable weights assigned to each concept class. By changing these weights, users can modify the ranking of the results in order to focus on documents dealing with a specific concept. The method's performance was evaluated using data from the 2004 TREC genomics track, producing a mean average precision of 0.425, with an average of 4.8 and 31.3 relevant documents within the top 10 and 100 retrieved abstracts, respectively. QuExT implements a concept-based query expansion scheme that leverages gene-related information available on a variety of biological resources. The main advantage of the system is to give the user control over the ranking of the results by means of a simple weighting scheme. Using this approach, researchers can effortlessly explore the literature regarding a group of genes and focus on the different aspects relating to these genes. Advances in biotechnology, together with the widespread use of high-throughput methods for gene analysis, have helped shifting the focus of biological research from specific genes and proteins to a more systemic analysis of the underlying biological problem. Researchers now face the increasing need to plan their experiments and analyse the resulting datasets in view of the quickly expanding biomedical information available [1, 2] . Although much of this knowledge is being annotated in the various existing biomedical databases, keeping these up-to-date is a difficult task due to the rapid emergence of new results and the exponential increase of published articles. Therefore, many relevant research outcomes are still enclosed as
Matos et al. REF developed a document retrieval and prioritization tool using concept-oriented query expansion to obtain documents with respect to related concepts.
Concept-based query expansion for retrieving gene related publications from MEDLINE
BMC Bioinformatics
BMC Bioinformatics
Computer Science Medicine