Cargando…
Neural networks with circular filters enable data efficient inference of sequence motifs
MOTIVATION: Nucleic acids and proteins often have localized sequence motifs that enable highly specific interactions. Due to the biological relevance of sequence motifs, numerous inference methods have been developed. Recently, convolutional neural networks (CNNs) have achieved state of the art perf...
Autores principales: | Blum, Christopher F, Kollmann, Markus |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6792110/ https://www.ncbi.nlm.nih.gov/pubmed/30918943 http://dx.doi.org/10.1093/bioinformatics/btz194 |
Ejemplares similares
-
Motifs enable communication efficiency and fault-tolerance in transcriptional networks
por: Roy, Satyaki, et al.
Publicado: (2020) -
Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics
por: Avecilla, Grace, et al.
Publicado: (2022) -
Horizontally Distributed Inference of Deep Neural Networks for AI-Enabled IoT
por: Rodriguez-Conde, Ivan, et al.
Publicado: (2023) -
Experimental noise cutoff boosts inferability of transcriptional networks in large-scale gene-deletion studies
por: Blum, C. F., et al.
Publicado: (2018) -
kmtricks: efficient and flexible construction of Bloom filters for large sequencing data collections
por: Lemane, Téo, et al.
Publicado: (2022)