Cargando…
Machine Learning for detection of viral sequences in human metagenomic datasets
BACKGROUND: Detection of highly divergent or yet unknown viruses from metagenomics sequencing datasets is a major bioinformatics challenge. When human samples are sequenced, a large proportion of assembled contigs are classified as “unknown”, as conventional methods find no similarity to known seque...
Autores principales: | Bzhalava, Zurab, Tampuu, Ardi, Bała, Piotr, Vicente, Raul, Dillner, Joakim |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6154907/ https://www.ncbi.nlm.nih.gov/pubmed/30249176 http://dx.doi.org/10.1186/s12859-018-2340-x |
Ejemplares similares
-
ViraMiner: Deep learning on raw DNA sequences for identifying viral genomes in human samples
por: Tampuu, Ardi, et al.
Publicado: (2019) -
Extension of the viral ecology in humans using viral profile hidden Markov models
por: Bzhalava, Zurab, et al.
Publicado: (2018) -
Perspective Taking in Deep Reinforcement Learning Agents
por: Labash, Aqeel, et al.
Publicado: (2020) -
Viruses in case series of tumors: Consistent presence in different cancers in the same subject
por: Arroyo Mühr, Laila Sara, et al.
Publicado: (2017) -
Multiagent cooperation and competition with deep reinforcement learning
por: Tampuu, Ardi, et al.
Publicado: (2017)