Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets |
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Authors: | Michael Gormley William Dampier Adam Ertel Bilge Karacali Aydin Tozeren |
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Institution: | (1) School of Biomedical Engineering, Drexel University, Philadelphia, PA, USA |
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Abstract: | Background Independently derived expression profiles of the same biological condition often have few genes in common. In this study,
we created populations of expression profiles from publicly available microarray datasets of cancer (breast, lymphoma and
renal) samples linked to clinical information with an iterative machine learning algorithm. ROC curves were used to assess
the prediction error of each profile for classification. We compared the prediction error of profiles correlated with molecular
phenotype against profiles correlated with relapse-free status. Prediction error of profiles identified with supervised univariate
feature selection algorithms were compared to profiles selected randomly from a) all genes on the microarray platform and
b) a list of known disease-related genes (a priori selection). We also determined the relevance of expression profiles on
test arrays from independent datasets, measured on either the same or different microarray platforms. |
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Keywords: | |
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