Association of genes to genetically inherited diseases using data mining

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
  • C Perez-Iratxeta
  • P Bork
  • MA Andrade
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000176494100023&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1038/ng895
eISSN
1546-1718
Externe Identifier
  • Clarivate Analytics Document Solution ID: 567PW
  • PubMed Identifier: 12006977
ISSN
1061-4036
Ausgabe der Veröffentlichung
3
Zeitschrift
NATURE GENETICS
Paginierung
316 - 319
Datum der Veröffentlichung
2002
Status
Published
Titel
Association of genes to genetically inherited diseases using data mining
Sub types
  • Article
Ausgabe der Zeitschrift
31

Datenquelle: Web of Science (Lite)

Andere Metadatenquellen:
Autoren
  • Carolina Perez-Iratxeta
  • Peer Bork
  • Miguel A Andrade
DOI
10.1038/ng895
eISSN
1546-1718
ISSN
1061-4036
Ausgabe der Veröffentlichung
3
Zeitschrift
Nature Genetics
Sprache
en
Online publication date
2002
Paginierung
316 - 319
Datum der Veröffentlichung
2002
Status
Published
Herausgeber
Springer Science and Business Media LLC
Herausgeber URL
http://dx.doi.org/10.1038/ng895
Datum der Datenerfassung
2023
Titel
Association of genes to genetically inherited diseases using data mining
Ausgabe der Zeitschrift
31

Datenquelle: Crossref

Abstract
Although approximately one-quarter of the roughly 4,000 genetically inherited diseases currently recorded in respective databases (LocusLink, OMIM) are already linked to a region of the human genome, about 450 have no known associated gene. Finding disease-related genes requires laborious examination of hundreds of possible candidate genes (sometimes, these are not even annotated; see, for example, refs 3,4). The public availability of the human genome draft sequence has fostered new strategies to map molecular functional features of gene products to complex phenotypic descriptions, such as those of genetically inherited diseases. Owing to recent progress in the systematic annotation of genes using controlled vocabularies, we have developed a scoring system for the possible functional relationships of human genes to 455 genetically inherited diseases that have been mapped to chromosomal regions without assignment of a particular gene. In a benchmark of the system with 100 known disease-associated genes, the disease-associated gene was among the 8 best-scoring genes with a 25% chance, and among the best 30 genes with a 50% chance, showing that there is a relationship between the score of a gene and its likelihood of being associated with a particular disease. The scoring also indicates that for some diseases, the chance of identifying the underlying gene is higher.
Addresses
  • European Molecular Biology Laboratory, Meyerhofstr.1, Heidelberg 69012, Germany.
Autoren
  • Carolina Perez-Iratxeta
  • Peer Bork
  • Miguel A Andrade
DOI
10.1038/ng895
eISSN
1546-1718
Externe Identifier
  • PubMed Identifier: 12006977
Open access
false
ISSN
1061-4036
Ausgabe der Veröffentlichung
3
Zeitschrift
Nature genetics
Schlüsselwörter
  • Humans
  • Genetic Diseases, Inborn
  • Genetic Predisposition to Disease
  • Genetic Markers
  • Chromosome Mapping
  • Phenotype
  • Genome, Human
  • Mathematics
  • MEDLINE
  • Databases, Genetic
Sprache
eng
Medium
Print-Electronic
Online publication date
2002
Paginierung
316 - 319
Datum der Veröffentlichung
2002
Status
Published
Datum der Datenerfassung
2002
Titel
Association of genes to genetically inherited diseases using data mining.
Sub types
  • Journal Article
Ausgabe der Zeitschrift
31

Datenquelle: Europe PubMed Central

Abstract
Although approximately one-quarter of the roughly 4,000 genetically inherited diseases currently recorded in respective databases (LocusLink, OMIM) are already linked to a region of the human genome, about 450 have no known associated gene. Finding disease-related genes requires laborious examination of hundreds of possible candidate genes (sometimes, these are not even annotated; see, for example, refs 3,4). The public availability of the human genome draft sequence has fostered new strategies to map molecular functional features of gene products to complex phenotypic descriptions, such as those of genetically inherited diseases. Owing to recent progress in the systematic annotation of genes using controlled vocabularies, we have developed a scoring system for the possible functional relationships of human genes to 455 genetically inherited diseases that have been mapped to chromosomal regions without assignment of a particular gene. In a benchmark of the system with 100 known disease-associated genes, the disease-associated gene was among the 8 best-scoring genes with a 25% chance, and among the best 30 genes with a 50% chance, showing that there is a relationship between the score of a gene and its likelihood of being associated with a particular disease. The scoring also indicates that for some diseases, the chance of identifying the underlying gene is higher.
Autoren
  • Carolina Perez-Iratxeta
  • Peer Bork
  • Miguel A Andrade
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/12006977
DOI
10.1038/ng895
ISSN
1061-4036
Ausgabe der Veröffentlichung
3
Zeitschrift
Nat Genet
Schlüsselwörter
  • Chromosome Mapping
  • Databases, Genetic
  • Genetic Diseases, Inborn
  • Genetic Markers
  • Genetic Predisposition to Disease
  • Genome, Human
  • Humans
  • MEDLINE
  • Mathematics
  • Phenotype
Sprache
eng
Country
United States
Paginierung
316 - 319
PII
ng895
Datum der Veröffentlichung
2002
Status
Published
Datum, an dem der Datensatz öffentlich gemacht wurde
2002
Titel
Association of genes to genetically inherited diseases using data mining.
Sub types
  • Journal Article
Ausgabe der Zeitschrift
31

Datenquelle: PubMed

Beziehungen:
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