ProDiGe: PRioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples

TL;DR

ProDiGe is a machine learning algorithm that effectively prioritizes disease genes by integrating diverse data sources and learning from positive and unlabeled examples, outperforming existing methods.

Contribution

ProDiGe introduces a novel positive-unlabeled learning approach for disease gene prioritization, enabling genome-wide searches and cross-disease information sharing.

Findings

ProDiGe outperforms state-of-the-art gene prioritization methods.

It effectively integrates multiple data sources.

The approach enables genome-wide disease gene searches.

Abstract

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.