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Last year, in 2011, we argued that biomedical informatics stands ready to revolutionize human health and healthcare using large-scale measurements on a large number of individuals. 1 We anticipated that, with the coming changes in the amount and diversity of datasets, data-centric approaches that compute on massive amounts of data (often called ‘Big Data’2, 3) to discover patterns and to make clinically relevant predictions would be increasingly common in translational bioinformatics. Given these trends, we programmed the 2012 Summit on Translational Bioinformatics to focus on research that takes us from base pairs to the bedside, 4 with a particular emphasis on clinical implications of mining massive datasets, and bridging the latest multimodal measurement technologies with the large amounts of electronic healthcare data that are increasingly available. The coming year did turn out to be the year of Big Data for the Summit, with multiple submissions on managing and interpreting large datasets (figure 1). Among the 35 full paper submissions to the Summit, four stood out for their innovation, and hence the authors were invited to expand the work for this special issue of JAMIA —adding to the growing presence of translational bioinformatics in the journal. 5–9 Figure 1 A tag cloud generated from the title and abstracts of the submissions made to the AMIA Translational Bioinformatics Summit 2012. The more frequently used the words are, the larger they appear. ‘Data’ was the most commonly mentioned word across all submissions for 2012. Liu et al 10 demonstrated how the ability to predict adverse drug reactions can be increased by integrating chemical, biological, and phenotypic properties of drugs. They demonstrated that prediction accuracy increased from 0. 9054 (when only chemical structures were used) to 0. 9524 (when chemical structures along with biological and phenotypic features were used). They conclude that data fusion … Correspondence to Dr Nigam H Shah, Stanford University School of Medicine, 1265 Welch Road, Room X-229, Stanford, CA 94305, USA; nigamatstanford. edu
Shah et al. (Fri,) studied this question.