Title :
Combining feature selection and DTW for time-varying functional genomics
Author :
Furlanello, Cesare ; Merler, Stefano ; Jurman, Giuseppe
Author_Institution :
ITC-irst, Trento, Italy
fDate :
6/1/2006 12:00:00 AM
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 allows the identification of a time-varying molecular profile of the ventricular remodeling process.
Keywords :
genetic engineering; genetics; learning (artificial intelligence); molecular biophysics; pattern clustering; signal processing; statistical analysis; time series; cardiogenomics PGA mouse model; dynamic time warping; feature selection algorithms; myocardial infarction; public microarray data; reconstruction method; signal processing; stagewise boosting; statistical machine learning; time-series component; time-varying functional genomics; time-varying molecular profile; ventricular remodeling process; Bioinformatics; Boosting; Cardiology; Data mining; Diversity reception; Electronics packaging; Genomics; Mice; Reconstruction algorithms; Time measurement; Clustering; genetics; pattern classification; time series;
Journal_Title :
Signal Processing, IEEE Transactions on
DOI :
10.1109/TSP.2006.873715