Given temporal high-throughput data defining a two-class functional genomic process, feature selection algorithms may be applied to extract a panel of discriminating gene time series from noise. We then aim at identifying the main trends of activity along the process: a reconstruction method based on stagewise boosting is endowed with a similarity measure based on the Dynamic Time Warping algorithm, defining a ranked set of time series component most contributing to the reconstruction. The approach is applied on synthetic and public microarray data. On the Cardiogenomics PGA mouse model of Myocardial Infarction, the approach allows the identification of a timevarying molecular profile of the ventricular remodeling process

Combining feature selection and DTW for time-varying functional genomics

Furlanello, Cesare;Merler, Stefano;Jurman, Giuseppe
2005

Abstract

Given temporal high-throughput data defining a two-class functional genomic process, feature selection algorithms may be applied to extract a panel of discriminating gene time series from noise. We then aim at identifying the main trends of activity along the process: a reconstruction method based on stagewise boosting is endowed with a similarity measure based on the Dynamic Time Warping algorithm, defining a ranked set of time series component most contributing to the reconstruction. The approach is applied on synthetic and public microarray data. On the Cardiogenomics PGA mouse model of Myocardial Infarction, the approach allows the identification of a timevarying molecular profile of the ventricular remodeling process
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/2645
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