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. We aim to identify the main trends of activity through time. A reconstruction method based on stagewise boosting is endowed with a similarity measure based on the dynamic time warping (DTW) algorithm, defining a ranked set of time-series component contributing most to the reconstruction. The approach is applied on synthetic and public microarray data. On the Cardiogenomics PGA Mouse Model of Myocardial Infarction, the approach al- lows the identification of a time-varying molecular profile of the ventricular remodeling process.

Combining feature selection and DTW for time-varying functional genomics

Furlanello, Cesare;Merler, Stefano;Jurman, Giuseppe
2006

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. We aim to identify the main trends of activity through time. A reconstruction method based on stagewise boosting is endowed with a similarity measure based on the dynamic time warping (DTW) algorithm, defining a ranked set of time-series component contributing most to the reconstruction. The approach is applied on synthetic and public microarray data. On the Cardiogenomics PGA Mouse Model of Myocardial Infarction, the approach al- lows the identification of a time-varying 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/11582/54002
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