Integration of Microarray and Textual Data Improves the Prognosis Prediction of Breast, Lung, and Ovarian Cancer PatientsO. Gevaert, S. Van Vooren, B. De Moor
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AbstractMicroarray data are notoriously noisy such that models predicting clinically rele- vant outcomes often contain many false positive genes. Integration of other data sources can alleviate this problem and enhance gene selection and model building. Probabilistic models provide a natural solution to integrate information by using the prior over model space. We investigated if the use of text information from PUBMED abstracts in the structure prior of a Bayesian network could improve the prediction of the prognosis in cancer. Our results show that prediction of the outcome with the text prior was signicantly better compared to not using a prior, both on a well known microarray data set and on three independent microarray data sets. | |
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