Convolutional neural network model to predict causal risk factors that share complex regulatory features

Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. Here, on the basis of >2000 functional features, we developed a convolutional neural net...

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Detalles Bibliográficos
Autores principales: Lee, Taeyeop, Sung, Min Kyung, Lee, Seulkee, Yang, Woojin, Oh, Jaeho, Kim, Jeong Yeon, Hwang, Seongwon, Ban, Hyo-Jeong, Choi, Jung Kyoon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Methods Online
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6902027/
https://www.ncbi.nlm.nih.gov/pubmed/31598692
http://dx.doi.org/10.1093/nar/gkz868

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6902027/
https://www.ncbi.nlm.nih.gov/pubmed/31598692
http://dx.doi.org/10.1093/nar/gkz868

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