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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Vydané v:	IET systems biology Ročník 8; číslo 2; s. 33 - 40
Hlavní autori:	Wu, Jiaxin, Yang, Silu, Jiang, Rui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England The Institution of Engineering and Technology 01.04.2014 The Institution of Engineering & Technology
Predmet:	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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