Classification and sparse-signature extraction from gene-expression data

In this work we suggest a statistical mechanics approach to the classification of high dimensional data according to a binary label. We propose an algorithm whose aim is twofold: first it learns a classifier from a relatively small number of data; second it extracts a sparse signature, i.e., a lower dimensional subspace carrying the information needed for classification. In particular the second part of the task is NP-hard; therefore we propose a statistical mechanics based message-passing approach. The resulting algorithm is tested on artificial data to prove its validity, but also to elucidate possible limitations. As an important application, we consider the classification of gene-expression data measured in various types of cancer tissues. We find that, despite the currently low quantity and quality of available data (the number of available samples is much smaller than the number of measured genes, thus strongly limiting the predictive capacities), the algorithm performs slightly better than many state-of-the-art approaches in bioinformatics