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 a...
In this paper we propose a novel data–driven kernel automatically determined by the training examples. Basically, it is built by combining a finite set of linearly independent functions, namely generalized terminated ramp functions, each depending on a pair of training data. When working in the Tikhonov regularization framework, the unique free parameter to be optimized is the regularizer, representing a trade-off between empirical error and smoothness of the solution
We describe a ranking method for the identification of biomedically important genes for diagnosis and therapy, and provide an experimental schema for the development of predictive classification models on microarray data. This combined approach is designed to correct the selection bias problem (too optimistic prediction errors estimated by rules tested on samples previously used in the feature selection process). Our E--RFE ranking algorithm is a wrapper based on the entropy of the distribut...