Summary
This paper reports a comparative study demonstrating what level of predictive performance can be achieved if class prediction is attempted based on features obtained as the top most differently expressed genes from class comparison studies. Several typically used methods of gene ranking in class comparison are considered including Wilcoxon rank test, signal to noise and fold-change method. Predictive performance is estimated for a variety of feature set dimensionalities, this allows to empirically find a classification model yielding best performance for new data. This is used as a measure of predictive performance of feature vectors. Predictive performance is illustrated using publicly available microarray data sets. Results are compared with those using feature selection methods aiming to reduce feature redundancy.
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Maciejewski, H. (2008). Predictive Performance of Top Differentially Expressed Genes in Microarray Gene Expression Studies. In: Pietka, E., Kawa, J. (eds) Information Technologies in Biomedicine. Advances in Soft Computing, vol 47. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68168-7_44
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DOI: https://doi.org/10.1007/978-3-540-68168-7_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68167-0
Online ISBN: 978-3-540-68168-7
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