In Silico Functional Assessment of Sequence Variations: Predicting Phenotypic Functions of Novel Variations
Genomics & Informatics
; : 166-172, 2008.
Article
Dans En
| WPRIM
| ID: wpr-203277
Responsable en Bibliothèque :
WPRO
ABSTRACT
A multitude of protein-coding sequence variations (CVs) in the human genome have been revealed as a result of major initiatives, including the Human Variome Project, the 1000 Genomes Project, and the International Cancer Genome Consortium. This naturally has led to debate over how to accurately assess the functional consequences of CVs, because predicting the functional effects of CVs and their relevance to disease phenotypes is becoming increasingly important. This article surveys and compares variation databases and in silico prediction programs that assess the effects of CVs on protein function. We also introduce a combinatorial approach that uses machine learning algorithms to improve prediction performance.
Texte intégral:
1
Indice:
WPRIM
Sujet Principal:
Phénotype
/
Simulation numérique
/
Génome humain
/
Génome
/
Substitution d'acide aminé
/
Mutation faux-sens
/
Apprentissage machine
Type d'étude:
Prognostic_studies
Limites du sujet:
Humans
langue:
En
Texte intégral:
Genomics & Informatics
Année:
2008
Type:
Article