Background: The rapid pace of bioscience research makes it very challenging to track relevant articles in one�s area\nof interest. MEDLINE, a primary source for biomedical literature, offers access to more than 20 million citations with\nthree-quarters of a million new ones added each year. Thus it is not surprising to see active research in building\nnew document retrieval and sentence retrieval systems. We present Ferret, a prototype retrieval system, designed\nto retrieve and rank sentences (and their documents) conveying gene-centric relationships of interest to a scientist.\nThe prototype has several features. For example, it is designed to handle gene name ambiguity and perform query\nexpansion. Inputs can be a list of genes with an optional list of keywords. Sentences are retrieved across species\nbut the species discussed in the records are identified. Results are presented in the form of a heat map and sentences\ncorresponding to specific cells of the heat map may be selected for display. Ferret is designed to assist bio scientists at\ndifferent stages of research from early idea exploration to advanced analysis of results from bench experiments.\nResults: Three live case studies in the field of plant biology are presented related to Arabidopsis thaliana. The first is to\ndiscover genes that may relate to the phenotype of open immature flower in Arabidopsis. The second case is about\nfinding associations reported between ethylene signaling and a set of 300+ Arabidopsis genes. The third case is on\nsearching for potential gene targets of an Arabidopsis transcription factor hypothesized to be involved in plant stress\nresponses. Ferret was successful in finding valuable information in all three cases. In the first case the bZIP family of\ngenes was identified. In the second case sentences indicating relevant associations were found in other species such as\npotato and jasmine. In the third sentences led to new research questions about the plant hormone salicylic acid.\nConclusions: Ferret successfully retrieved relevant gene-centric sentences from PubMed records. The three case studies\ndemonstrate end user satisfaction with the system.
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