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The Complexity of Some Pattern Problems in the Logical Analysis of Large Genomic Data Sets Read chapter Read first chapter

2016 | OriginalPaper | Chapter

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

Authors : Marco Frasca, Simone Bassis

Published in: Bioinformatics and Biomedical Engineering

Publisher: Springer International Publishing

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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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previous chapter A Machine Learning Methodology for Enzyme Functional Classification Combining Structural and Protein Sequence Descriptors
next chapter Network Ranking Assisted Semantic Data Mining
Footnotes
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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Metadata
Title
Gene-Disease Prioritization Through Cost-Sensitive Graph-Based Methodologies
Authors
Marco Frasca
Simone Bassis
Copyright Year
2016
Book
Bioinformatics and Biomedical Engineering

Print ISBN: 978-3-319-31743-4

Electronic ISBN: 978-3-319-31744-1

Copyright Year: 2016

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

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