Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Tavares, V. , Monteiro, J., Vassos, E., Coleman, J. & Prata, D. (2021). Evaluation of Genotype-Based Gene Expression Model Performance: A cross-framework and cross-dataset study. Genes. 12 (10)
Exportar Referência (IEEE) 
V. Tavares et al.,  "Evaluation of Genotype-Based Gene Expression Model Performance: A cross-framework and cross-dataset study", in Genes, vol. 12, no. 10, 2021
Exportar BibTeX 
@article{tavares2021_1731964922012,
	author = "Tavares, V.  and Monteiro, J. and Vassos, E. and Coleman, J. and Prata, D.",
	title = "Evaluation of Genotype-Based Gene Expression Model Performance: A cross-framework and cross-dataset study",
	journal = "Genes",
	year = "2021",
	volume = "12",
	number = "10",
	doi = "10.3390/genes12101531",
	url = "https://www.mdpi.com/journal/genes"
}
Exportar RIS 
TY  - JOUR
TI  - Evaluation of Genotype-Based Gene Expression Model Performance: A cross-framework and cross-dataset study
T2  - Genes
VL  - 12
IS  - 10
AU  - Tavares, V. 
AU  - Monteiro, J.
AU  - Vassos, E.
AU  - Coleman, J.
AU  - Prata, D.
PY  - 2021
SN  - 2073-4425
DO  - 10.3390/genes12101531
UR  - https://www.mdpi.com/journal/genes
AB  - Predicting gene expression from genotyped data is valuable for studying inaccessible tissues such as the brain. Herein we present eGenScore, a polygenic/poly-variation method, and compare it with PrediXcan, a method based on regularized linear regression using elastic nets. While both methods have the same purpose of predicting gene expression based on genotype, they carry important methodological differences. We compared the performance of expression quantitative trait loci (eQTL) models to predict gene expression in the frontal cortex, comparing across these frameworks (eGenScore vs. PrediXcan) and training datasets (BrainEAC, which is brain-specific, vs. GTEx, which has data across multiple tissues). In addition to internal five-fold cross-validation, we externally validated the gene expression models using the CommonMind Consortium database. Our results showed that (1) PrediXcan outperforms eGenScore regardless of the training database used; and (2) when using PrediXcan, the performance of the eQTL models in frontal cortex is higher when trained with GTEx than with BrainEAC. 
ER  -