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Gene-Disease Prioritization Through Cost-Sensitive Graph-Based Methodologies

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Bioinformatics and Biomedical Engineering (IWBBIO 2016)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9656))

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Abstract

Finding genes associated with human genetic disorders is one of the most challenging problems in bio-medicine. In this context, to guide researchers in detecting the most reliable candidate causative-genes for the disease of interest, gene prioritization methods represent a necessary support to automatically rank genes according to their involvement in the disease under study. This problem is characterized by highly unbalanced classes (few causative and much more non-causative genes) and requires the adoption of cost-sensitive techniques to achieve reliable solutions. In this work we propose a network-based methodology for disease-gene prioritization designed to expressly cope with the data imbalance. Its validation over a benchmark composed of 708 selected medical subject headings (MeSH) diseases, shows that our approach is competitive with state-of-art methodologies, and its reduced time complexity makes its application feasible on large-size datasets.

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Notes

  1. 1.

    http://www.nlm.nih.gov/mesh.

  2. 2.

    Actually the number of predictors, including the two-way interaction term (i.e. the product of the two features), is equal to 3.

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Authors and Affiliations

  1. Department of Computer Science, University of Milano, Via Comelico 39/41, 20135, Milano, Italy

    Marco Frasca & Simone Bassis

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  1. Marco Frasca
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  2. Simone Bassis
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Correspondence to Marco Frasca .

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Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Francisco Ortuño

  2. Universidad de Granada, Granada, Spain

    Ignacio Rojas

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Frasca, M., Bassis, S. (2016). Gene-Disease Prioritization Through Cost-Sensitive Graph-Based Methodologies. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_64

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  • DOI: https://doi.org/10.1007/978-3-319-31744-1_64

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-31744-1

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