NCBoost classifies pathogenic non-coding variants in Mendelian diseases through supervised learning on purifying selection signals in humans.
Genome Biol
; 20(1): 32, 2019 02 11.
Article
en En
| MEDLINE
| ID: mdl-30744685
ABSTRACT
State-of-the-art methods assessing pathogenic non-coding variants have mostly been characterized on common disease-associated polymorphisms, yet with modest accuracy and strong positional biases. In this study, we curated 737 high-confidence pathogenic non-coding variants associated with monogenic Mendelian diseases. In addition to interspecies conservation, a comprehensive set of recent and ongoing purifying selection signals in humans is explored, accounting for lineage-specific regulatory elements. Supervised learning using gradient tree boosting on such features achieves a high predictive performance and overcomes positional bias. NCBoost performs consistently across diverse learning and independent testing data sets and outperforms other existing reference methods.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Selección Genética
/
Polimorfismo de Nucleótido Simple
/
ADN Intergénico
/
Aprendizaje Automático Supervisado
/
Enfermedades Genéticas Congénitas
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Genome Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
/
GENETICA
Año:
2019
Tipo del documento:
Article
País de afiliación:
Francia