Abstract
Microarray analysis has become a significant use of machine learning in molecular biology. Datasets obtained from this method consist of tens of thousands of attributes usually describing tens of objects. Such setting makes the use of some form of feature selection an inevitable step of analysis—mostly to reduce the feature set to manageable size, but also to obtain an biological insight in the mechanisms of the investigated process. In this paper we present a reanalysis of a previously published late radiation toxicity prediction problem. On that lurid example we show how futile it may be to rely on non-validated feature selection and how even advanced algorithms fail to distinguish between noise and signal when the latter is weak. We also propose methods of detecting and dealing with mentioned problems.
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Kursa, M.B., Rudnicki, W.R. (2011). A Deceiving Charm of Feature Selection: The Microarray Case Study. In: Czachórski, T., Kozielski, S., Stańczyk, U. (eds) Man-Machine Interactions 2. Advances in Intelligent and Soft Computing, vol 103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23169-8_16
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DOI: https://doi.org/10.1007/978-3-642-23169-8_16
Publisher Name: Springer, Berlin, Heidelberg
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