Inferring non-synonymous single-nucleotide polymorphisms-disease associations via integration of multiple similarity networks

Detecting associations between human genetic variants and their phenotypic effects is a significant problem in understanding genetic bases of human-inherited diseases. The focus is on a typical type of genetic variants called non-synonymous single nucleotide polymorphisms (nsSNPs), whose occurrence...

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Veröffentlicht in:IET systems biology Jg. 8; H. 2; S. 33 - 40
Hauptverfasser: Wu, Jiaxin, Yang, Silu, Jiang, Rui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England The Institution of Engineering and Technology 01.04.2014
The Institution of Engineering & Technology
Schlagworte:
Algorithms
Area Under Curve
binary classification problem
Binding Sites
candidate nsSNP
Computational Biology - methods
disease associations
disease phenotype similarity networks
diseases
Genetic Diseases, Inborn - genetics
Genetic Predisposition to Disease
Genetic Variation
genetics
human genetic variants
Humans
human‐inherited diseases
multiple similarity network integration
Mutation
nonsynonymous single nucleotide polymorphisms
Phenotype
phenotypic effects
polymorphism
Polymorphism, Single Nucleotide
protein sequence
proteins
ROC Curve
Software
Special Issue: Part 1: Network Biology in Translational Bioinformatics and Systems Biology
structure information
unknown genetic bases
binary classification problem
disease associations
diseases
polymorphism
unknown genetic bases
human genetic variants
disease phenotype similarity networks
nonsynonymous single nucleotide polymorphisms
candidate nsSNP
human-inherited diseases
nonsynonymous single-nucleotide polymorphisms
genetics
multiple similarity network integration
proteins
structure information
phenotypic effects
protein sequence
ISSN:1751-8849, 1751-8857
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Zusammenfassung:Detecting associations between human genetic variants and their phenotypic effects is a significant problem in understanding genetic bases of human-inherited diseases. The focus is on a typical type of genetic variants called non-synonymous single nucleotide polymorphisms (nsSNPs), whose occurrence may potentially alter the structures of proteins, affecting functions of proteins, and thereby causing diseases. Most of the existing methods predict associations between nsSNPs and diseases based on features derived from only protein sequence and/or structure information, and give no information about which specific disease an nsSNP is associated with. To cope with these problems, the identification of nsSNPs that are associated with a specific disease from a set of candidate nsSNPs as a binary classification problem has been formulated. A new approach has been adopted for predicting associations between nsSNPs and diseases based on multiple nsSNP similarity networks and disease phenotype similarity networks. With a series of comprehensive validation experiments, it has been demonstrated that the proposed method is effective in both recovering the nsSNP-disease associations and inferring suspect disease-associated nsSNPs for both diseases with known genetic bases and diseases of unknown genetic bases.
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ISSN:1751-8849
1751-8857
DOI:10.1049/iet-syb.2013.0033