Text-derived concept profiles support assessment of DNA microarray data for acute myeloid leukemia and for androgen receptor sti

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. Results: The experimental validation was done in two steps. We first applied our method on a controlled test set. After this proved to be successful the datasets from two DNA microarray experiments were analyzed in the same way and the results were evaluated by domain experts. The first dataset was a gene-ex...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Rob Jelier, Guido Jenster, Lambert C. J. Dorssers, Bas J. Wouters, Peter J. M. Hendriksen, Barend Mons, Ruud Delwel, Jan A. Kors
• Keywords: BMCBI 2007, Cancer Cell, DNA Microarray, Profile