ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples.
ProDiGe: Prioritization Of Disease Genes with multitask machine learning from positive and unlabeled examples.
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DOI：
10.1186/1471-2105-12-389
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发表时间：
2011-10-06
影响因子：
3
通讯作者：
Vert JP
中科院分区：
文献类型：
--
作者：
Mordelet F;Vert JP
阐明人类疾病的遗传基础是遗传学和分子生物学的中心目标。虽然传统的连锁分析和现代高通量技术经常提供数十或数百个候选疾病基因的长长名单，但在候选基因中识别疾病基因仍然既耗时又昂贵。因此，需要有效的计算方法，通过利用各种数据库中关于基因的丰富信息，对候选列表中的基因进行优先排序。我们提出了一种新的疾病基因排序算法PRODGE。Prodige实施了一种新的机器学习策略，该策略基于对阳性和未标记示例的学习，允许集成有关基因的各种信息源，在不同疾病之间共享有关已知疾病基因的信息，并执行全基因组搜索新的疾病基因。对真实数据的实验表明，PRODGE在人类疾病基因优先排序方面的表现优于最先进的方法。Prodige实施了一种新的机器学习范式来确定基因的优先顺序，这可能有助于识别新的疾病基因。它可以在http://cbio.ensmp.fr/prodige.上免费获得
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. 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. ProDiGe implements a new machine learning paradigm for gene prioritization, which could help the identification of new disease genes. It is freely available at http://cbio.ensmp.fr/prodige.
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