Cargando…
Approximate inference of gene regulatory network models from RNA-Seq time series data
BACKGROUND: Inference of gene regulatory network structures from RNA-Seq data is challenging due to the nature of the data, as measurements take the form of counts of reads mapped to a given gene. Here we present a model for RNA-Seq time series data that applies a negative binomial distribution for...
Autor principal: | Thorne, Thomas |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5896118/ https://www.ncbi.nlm.nih.gov/pubmed/29642837 http://dx.doi.org/10.1186/s12859-018-2125-2 |
Ejemplares similares
-
Inference of gene regulatory networks from time series by Tsallis entropy
por: Lopes , Fabrício Martins, et al.
Publicado: (2011) -
Function approximation approach to the inference of reduced NGnet models of genetic networks
por: Kimura, Shuhei, et al.
Publicado: (2008) -
ATAC2GRN: optimized ATAC-seq and DNase1-seq pipelines for rapid and accurate genome regulatory network inference
por: Pranzatelli, Thomas J. F., et al.
Publicado: (2018) -
Inferring microRNA and transcription factor regulatory networks in heterogeneous data
por: Le, Thuc D, et al.
Publicado: (2013) -
Statistical inference for time course RNA-Seq data using a negative binomial mixed-effect model
por: Sun, Xiaoxiao, et al.
Publicado: (2016)