Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks

MOTIVATION: Regulatory sequences are not solely defined by their nucleic acid sequence but also by their relative distances to genomic landmarks such as transcription start site, exon boundaries or polyadenylation site. Deep learning has become the approach of choice for modeling regulatory sequence...

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Detalles Bibliográficos
Autores principales: Avsec, Žiga, Barekatain, Mohammadamin, Cheng, Jun, Gagneur, Julien
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Original Papers
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5905632/
https://www.ncbi.nlm.nih.gov/pubmed/29155928
http://dx.doi.org/10.1093/bioinformatics/btx727

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5905632/
https://www.ncbi.nlm.nih.gov/pubmed/29155928
http://dx.doi.org/10.1093/bioinformatics/btx727

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