Cargando…
Inferring disease progression and gene regulatory networks from clinical transcriptomic data using PROB_R
Due to a lack of explicit temporal information, it can be challenging to infer gene regulatory networks from clinical transcriptomic data. Here, we describe the protocol of PROB_R for inferring latent temporal disease progression and reconstructing gene regulatory networks from cross-sectional clini...
Autores principales: | Dong, Zhaorui, Sun, Xiaoqiang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9207570/ https://www.ncbi.nlm.nih.gov/pubmed/35733604 http://dx.doi.org/10.1016/j.xpro.2022.101467 |
Ejemplares similares
-
The matlockite-type praseodymium(III) oxide bromide PrOBr
por: Talmon-Gros, Pia, et al.
Publicado: (2011) -
Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples
por: Sun, Xiaoqiang, et al.
Publicado: (2021) -
Intention Recognition With ProbLog
por: Smith, Gary B., et al.
Publicado: (2022) -
Multi-omics regulatory network inference in the presence of missing data
por: Henao, Juan D, et al.
Publicado: (2023) -
ProbC: joint modeling of epigenome and transcriptome effects in 3D genome
por: Sefer, Emre
Publicado: (2022)