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Indexed/Abstracted in: EMBASE, Science Citation Index Expanded (SciSearch), Scopus
Impact Factor 0,246
Online ISSN 1827-160X
MICROARRAY MEETING 2003: III CONVEGNO ITALIANO SULLA TECNOLOGIA
Segrate (MI), 9-10 Giugno 2003
Furlanello C., Serafini M., Merler S., Jurman G.
ITC-IRST, Trento, Italy
We present an experimental set-up for the analysis and prediction on microarray data specifically designed to identify and correct the impact of selection bias in high-throughput problems. A number of recently published and overoptimistic studies present feature selection and gene profiling processes that incur overfitting effects. We outline the selection bias problem and demonstrate its effect on synthetic and microarray data. We then introduce and describe a procedure that successfully deals with the problem through extensive resampling and label randomization techniques that employ support vector machines as a base classifier and an improved version of the recursive feature elimination algorithm for gene ranking.