Linked Life Data

19514061

Source:http://linkedlifedata.com/resource/pubmed/id/19514061

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
8
pubmed:dateCreated
2009-7-28
pubmed:abstractText
Single nucleotide polymorphisms (SNPs) are the simplest and most frequent form of human DNA variation, also valuable as genetic markers of disease susceptibility. The most investigated SNPs are missense mutations resulting in residue substitutions in the protein. Here we propose SNPs&GO, an accurate method that, starting from a protein sequence, can predict whether a mutation is disease related or not by exploiting the protein functional annotation. The scoring efficiency of SNPs&GO is as high as 82%, with a Matthews correlation coefficient equal to 0.63 over a wide set of annotated nonsynonymous mutations in proteins, including 16,330 disease-related and 17,432 neutral polymorphisms. SNPs&GO collects in unique framework information derived from protein sequence, evolutionary information, and function as encoded in the Gene Ontology terms, and outperforms other available predictive methods.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1098-1004
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
30
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1237-44
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Functional annotations improve the predictive score of human disease-related mutations in proteins.
pubmed:affiliation
Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, Bologna 40126, Italy.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't