In Silico Functional Assessment of Sequence Variations: Predicting Phenotypic Functions of Novel Variations
Genomics & Informatics
;
: 166-172, 2008.
Article
in English
| WPRIM
| ID: wpr-203277
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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Phenotype
/
Computer Simulation
/
Genome, Human
/
Genome
/
Amino Acid Substitution
/
Mutation, Missense
/
Machine Learning
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal:
Genomics & Informatics
Year:
2008
Type:
Article
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