Combining Feature Selection and DTW for Time-Varying Functional Genomics

摘要

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 allows the identification of a time-varying molecular profile of the ventricular remodeling process.