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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2018
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| Materias: | |
| 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 |