  Elucidating the genetic basis of human diseases is a central goal of genetics
and molecular biology. While traditional linkage analysis and modern
high-throughput techniques often provide long lists of tens or hundreds of
disease gene candidates, the identification of disease genes among the
candidates remains time-consuming and expensive. Efficient computational
methods are therefore needed to prioritize genes within the list of candidates,
by exploiting the wealth of information available about the genes in various
databases. Here we propose ProDiGe, a novel algorithm for Prioritization of
Disease Genes. ProDiGe implements a novel machine learning strategy based on
learning from positive and unlabeled examples, which allows to integrate
various sources of information about the genes, to share information about
known disease genes across diseases, and to perform genome-wide searches for
new disease genes. Experiments on real data show that ProDiGe outperforms
state-of-the-art methods for the prioritization of genes in human diseases.
