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

By Fantine Mordelet, Jean-Philippe Vert. 2011

183 citations recorded by the source corpus.

BackgroundElucidating 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.ResultsWe 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.ConclusionsProDiGe 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.

Subfields: Genetics, Molecular Biology, Reinforcement Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Jessa Bekker
2. Jesse Davis
3. Nagarajan Natarajan
4. Qiang Yang
5. Inderjit S. Dhillon
6. Yu Zhang

In Molecular Biology

1. Qiang Yang
2. Yu Zhang
3. Jessa Bekker
4. Jesse Davis
5. Nagarajan Natarajan
6. Inderjit S. Dhillon
7. Pavel Berkhin
8. Virginia de Sa

In Reinforcement Learning

1. Jessa Bekker
2. Jesse Davis
3. Nagarajan Natarajan
4. Qiang Yang
5. Inderjit S. Dhillon
6. Yu Zhang

Citations in the corpus

• An Overview of Multi-task Learning (2018, background): “…in Alzheimer’s disease [109], identification of longitudinal phenotypic markers for Alzheimer’s disease progression prediction [110], prioritization of disease genes [111], biological image analysis based on natural images [20], survival analysis [112], and multiple genetic trait prediction [113].”
• Inductive matrix completion for predicting gene–disease associations (2014, methodology): “Recent methods including ProDiGe [Mordelet and Vert, 2011] and CATAPULT [Singh-Blom et al.”
• Learning from positive and unlabeled data: a survey (2018, methodology): “l of the candidates individually would be very costly. With PU learning, a promising subset can be discovered. Several PU methods were developed to this end: ProDiGe is a method based on bagging SVMs [70,72], PUDI is also a weighted SVM method, but they have dierent weights for four identied groups ”