Prediction of RNA subcellular localization: Learning from heterogeneous data sources
Summary: RNA subcellular localization has recently emerged as a widespread phenomenon, which may apply to the majority of RNAs. The two main sources of data for characterization of RNA localization are sequence features and microscopy images, such as obtained from single-molecule fluorescent in situ...
Guardado en:
Autores principales: | Anca Flavia Savulescu, Emmanuel Bouilhol, Nicolas Beaume, Macha Nikolski |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Elsevier
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/c13daf0585c841d29ced38766221a951 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Are we there yet? A machine learning architecture to predict organotropic metastases
por: Michael Skaro, et al.
Publicado: (2021) -
Machine Learning-Based Identification of Potentially Novel Non-Alcoholic Fatty Liver Disease Biomarkers
por: Roshan Shafiha, et al.
Publicado: (2021) -
Comparative Transcriptomic and Functional Assessments of Linezolid-Responsive Small RNA Genes in <named-content content-type="genus-species">Staphylococcus aureus</named-content>
por: Wei Gao, et al.
Publicado: (2020) -
The <named-content content-type="genus-species">Staphylococcus aureus</named-content> Transcriptome during Cystic Fibrosis Lung Infection
por: Carolyn B. Ibberson, et al.
Publicado: (2019) -
Integrated Analysis of Mutations, miRNA and mRNA Expression in Glioblastoma
por: Wang S, et al.
Publicado: (2021)