Candidate gene prioritization by genomic data fusion

author: Yves Moreau, Department of Electrical Engineering, KU Leuven
published: Nov. 20, 2007,   recorded: September 2007,   views: 421453


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The overwhelming amount of biological data makes the assignment of candidate genes to diseases and biological pathways a formidable challenge. We present ENDEAVOUR, a generally applicable computational methodology to prioritize candidate genes based on their similarity to case-specific reference gene sets. Unlike previous methods, ENDEAVOUR is capable of flexibly utilizing multiple data sets from diverse sources. It allows the modular incorporation of de novo generated data sets and integrates distinct prioritizations into a global ranking by applying order statistics. We first validate the overallperformance in a statistical cross validation of 29 diseases and 3 biological pathways. We validate a novel candidate for DiGeorge syndrome in a zebrafish model and present several new candidates for congenital heart disease. We extend the basic ENDEAVOUR methodology using data from multiple species (human, mouse, rat, drosophila and C. elegans). We also present an alternative machine learning methodology for gene prioritization using kernel methods for novelty detection that outperforms our previous results.

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Comment1 Mathew Hyden, November 18, 2021 at 10:10 a.m.:

A wonderful lecture was delivered by Yves Moreau on one of the most complex topics in biology. One of my friends asked me to visit this website to watch the entire lecture repeatedly when I wasn't feeling well during my college lecture. Now I will visit website to read people's feedback about essay writers before hiring them to assist me in my essay that I would like to write on this topic.

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