Cargando…
Single-cell gene regulatory network prediction by explainable AI
The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding of this heterogeneity, it offers a mostly descript...
Autores principales: | Keyl, Philipp, Bischoff, Philip, Dernbach, Gabriel, Bockmayr, Michael, Fritz, Rebecca, Horst, David, Blüthgen, Nils, Montavon, Grégoire, Müller, Klaus-Robert, Klauschen, Frederick |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9976884/ https://www.ncbi.nlm.nih.gov/pubmed/36629274 http://dx.doi.org/10.1093/nar/gkac1212 |
Ejemplares similares
-
Patient-level proteomic network prediction by explainable artificial intelligence
por: Keyl, Philipp, et al.
Publicado: (2022) -
Explainable AI
por: Samek, Wojciech, et al.
Publicado: (2019) -
Computational analysis reveals histotype-dependent molecular profile and actionable mutation effects across cancers
por: Heim, Daniel, et al.
Publicado: (2018) -
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
por: Bach, Sebastian, et al.
Publicado: (2015) -
Leveraging explainable AI for gut microbiome-based colorectal cancer classification
por: Rynazal, Ryza, et al.
Publicado: (2023)