Abstract
Disease candidate gene prioritization addresses the association of novel genes with disease susceptibility or progression. Networkbased approaches explore the connectivity properties of biological networks to compute an association score between candidate and diseaserelated genes. Although several methods have been proposed to date, a number of concerns arise: (i) most networks used rely exclusively on curated physical interactions, resulting in poor coverage of the Human genome and leading to sparsity issues; (ii) most methods fail to incorporate interaction confidence weights; (iii) in some cases, relevance scores are computed as local measures based on the direct interactions with the disease-related genes, ignoring potentially relevant indirect interactions. In this study, we seek a robust network-based strategy by evaluating the performance of selected prioritization strategies using genes known to be involved in 29 different diseases.
Original language | English |
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Pages (from-to) | 53-64 |
Number of pages | 12 |
Journal | CEUR Workshop Proceedings |
Volume | 655 |
Publication status | Published - 2010 |
Externally published | Yes |
Event | 1st Workshop on Dynamic Networks and Knowledge Discovery, DyNaK 2010 - Barcelona, Spain Duration: 24 Sept 2010 → 24 Sept 2010 |
Keywords
- Disease candidate genes
- Network
- Prioritization
- Protein-protein interaction
- Random walk