Applying feature selection methods on fMRI data

In neuroscience, the ability to correlate and classify certain activity patterns of the brain to different physical and mental states of the subject is of high importance. Analysis of fMRI data is one of the venues in which this objective is being pursued. However data produced using fMRI technology is highly complex. To this end, machine...

Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis

Integrating gene expression data with secondary data such as pathway or protein-protein interaction data has been proposed as a promising approach for improved outcome prediction of cancer patients. Methods employing this approach usually aggregate the expression of genes into new composite features, while the secondary data guide this...

Prognostic Molecular Classification of Breast Cancer Based on Features Extracted from a Scale Space

Breast cancer is one of the most prevalent cancers affecting females in the world. In recent years, many cancer researchers have been trying to determine molecular prognosis tools that predict cancer patient treatment response and/or chance of survival. In particular, the determination of gene expression signatures obtained by feature selection...