标题： ProDiGe：基于多任务机器学习从正例和未标注例中优先排序疾病基因 显示英文标题

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

摘要： 阐明人类疾病的遗传基础是遗传学和分子生物学的核心目标。尽管传统的连锁分析和现代高通量技术通常会提供数十个甚至数百个疾病基因候选者名单，但从这些候选者中识别疾病基因仍然耗时且昂贵。因此，迫切需要高效计算方法，通过利用各种数据库中关于基因的丰富信息来优先排序候选者列表中的基因。本文提出了一种名为ProDiGe的新算法，用于疾病基因的优先排序。ProDiGe采用了一种基于从正例和未标记样本中学习的新机器学习策略，允许整合关于基因的各种信息来源，共享已知疾病基因在不同疾病之间的信息，并进行全基因组范围内的新疾病基因搜索。实验证明，ProDiGe在人类疾病中基因优先排序方面优于现有的最先进的方法。 显示英文摘要

摘要： 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.