ArticleAmerican journal of human genetics2007
Sequence-based prioritization of nonsynonymous single-nucleotide polymorphisms for the study of disease mutations.
Article in American journal of human genetics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Article
- A sequence-based method to predict the impact of regulatory variants using random forest.BMC systems biology · 2017Article
- Global inference of disease-causing single nucleotide variants from exome sequencing data.BMC bioinformatics · 2016Article
- Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications.Frontiers in genetics · 2016Review
- dbWGFP: a database and web server of human whole-genome single nucleotide variants and their functional predictions.Database : the journal of biological databases and curation · 2016Article
- Prioritization Of Nonsynonymous Single Nucleotide Variants For Exome Sequencing Studies Via Integrative Learning On Multiple Genomic Data.Scientific reports · 2015Article
- GESPA: classifying nsSNPs to predict disease association.BMC bioinformatics · 2015Article
- Inferring non-synonymous single-nucleotide polymorphisms-disease associations via integration of multiple similarity networks.IET systems biology · 2014Article
- Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.PLoS genetics · 2014Article
- Prioritizing protein complexes implicated in human diseases by network optimization.BMC systems biology · 2014Article
- CoDP: predicting the impact of unclassified genetic variants in MSH6 by the combination of different properties of the protein.Journal of biomedical science · 2013Article
- Exploring functional variant discovery in non-coding regions with SInBaD.Nucleic acids research · 2013Article
- Prediction of deleterious nonsynonymous single-nucleotide polymorphism for human diseases.TheScientificWorldJournal · 2013Review
- Phylomedicine: an evolutionary telescope to explore and diagnose the universe of disease mutations.Trends in genetics : TIG · 2011Review
- Human allelic variation: perspective from protein function, structure, and evolution.Current opinion in structural biology · 2010Review
- Analytical methods for inferring functional effects of single base pair substitutions in human cancers.Human genetics · 2009Review
- Detecting disease-associated genotype patterns.BMC bioinformatics · 2009Article
- A random forest approach to the detection of epistatic interactions in case-control studies.BMC bioinformatics · 2009Article
- Learning a prior on regulatory potential from eQTL data.PLoS genetics · 2009Article
- Systematic biological prioritization after a genome-wide association study: an application to nicotine dependence.Bioinformatics (Oxford, England) · 2008Article
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6 authors.
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Abstract
The increasing demand for the identification of genetic variation responsible for common diseases has translated into a need for sophisticated methods for effectively prioritizing mutations occurring in disease-associated genetic regions. In this article, we prioritize candidate nonsynonymous single-nucleotide polymorphisms (nsSNPs) through a bioinformatics approach that takes advantages of a set of improved numeric features derived from protein-sequence information and a new statistical learning model called "multiple selection rule voting" (MSRV). The sequence-based features can maximize the scope of applications of our approach, and the MSRV model can capture subtle characteristics of individual mutations. Systematic validation of the approach demonstrates that this approach is capable of prioritizing causal mutations for both simple monogenic diseases and complex polygenic diseases. Further studies of familial Alzheimer diseases and diabetes show that the approach can enrich mutations underlying these polygenic diseases among the top of candidate mutations. Application of this approach to unclassified mutations suggests that there are 10 suspicious mutations likely to cause diseases, and there is strong support for this in the literature.
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