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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Vydáno v:	IET systems biology Ročník 8; číslo 2; s. 33 - 40
Hlavní autoři:	Wu, Jiaxin, Yang, Silu, Jiang, Rui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England The Institution of Engineering and Technology 01.04.2014 The Institution of Engineering & Technology
Témata:	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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Abstract	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.
AbstractList	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. 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 nonsynonymous single nucleotide polymorphisms (nsSNP), 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. 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. 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.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.
Author	Yang, Silu Jiang, Rui Wu, Jiaxin
AuthorAffiliation	1 MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation Tsinghua University Beijing 100084 People's Republic of China
AuthorAffiliation_xml	– name: 1 MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation Tsinghua University Beijing 100084 People's Republic of China
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Snippet	Detecting associations between human genetic variants and their phenotypic effects is a significant problem in understanding genetic bases of human-inherited... Detecting associations between human genetic variants and their phenotypic effects is a significant problem in understanding genetic bases of human‐inherited...
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SubjectTerms	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
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Title	Inferring non-synonymous single-nucleotide polymorphisms-disease associations via integration of multiple similarity networks
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