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Prediction of Deleterious Nonsynonymous Single-Nucleotide Polymorphism for Human Diseases
Joint Authors
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-01-30
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
The identification of genetic variants that are responsible for human inherited diseases is a fundamental problem in human and medical genetics.
As a typical type of genetic variation, nonsynonymous single-nucleotide polymorphisms (nsSNPs) occurring in protein coding regions may alter the encoded amino acid, potentially affect protein structure and function, and further result in human inherited diseases.
Therefore, it is of great importance to develop computational approaches to facilitate the discrimination of deleterious nsSNPs from neutral ones.
In this paper, we review databases that collect nsSNPs and summarize computational methods for the identification of deleterious nsSNPs.
We classify the existing methods for characterizing nsSNPs into three categories (sequence based, structure based, and annotation based), and we introduce machine learning models for the prediction of deleterious nsSNPs.
We further discuss methods for identifying deleterious nsSNPs in noncoding variants and those for dealing with rare variants.
American Psychological Association (APA)
Wu, Jiaxin& Jiang, Rui. 2013. Prediction of Deleterious Nonsynonymous Single-Nucleotide Polymorphism for Human Diseases. The Scientific World Journal،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1033180
Modern Language Association (MLA)
Wu, Jiaxin& Jiang, Rui. Prediction of Deleterious Nonsynonymous Single-Nucleotide Polymorphism for Human Diseases. The Scientific World Journal No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-1033180
American Medical Association (AMA)
Wu, Jiaxin& Jiang, Rui. Prediction of Deleterious Nonsynonymous Single-Nucleotide Polymorphism for Human Diseases. The Scientific World Journal. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-1033180
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1033180