Text-derived concept profiles support assessment of DNA microarray data for acute myeloid leukemia and for androgen receptor stimulation |
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| Authors: | Rob Jelier Guido Jenster Lambert CJ Dorssers Bas J Wouters Peter JM Hendriksen Barend Mons Ruud Delwel Jan A Kors |
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| Affiliation: | (1) Department of Medical Informatics, Erasmus MC – University Medical Center, Rotterdam, The Netherlands;(2) Department of Urology, Erasmus MC – University Medical Center, Rotterdam, The Netherlands;(3) Department of Pathology, Erasmus MC – University Medical Center, Rotterdam, The Netherlands;(4) Department of Hematology, Erasmus MC – University Medical Center, Rotterdam, The Netherlands |
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| Abstract: | Background High-throughput experiments, such as with DNA microarrays, typically result in hundreds of genes potentially relevant to the process under study, rendering the interpretation of these experiments problematic. Here, we propose and evaluate an approach to find functional associations between large numbers of genes and other biomedical concepts from free-text literature. For each gene, a profile of related concepts is constructed that summarizes the context in which the gene is mentioned in literature. We assign a weight to each concept in the profile based on a likelihood ratio measure. Gene concept profiles can then be clustered to find related genes and other concepts. |
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