Cargando…
Inference of Gene Regulatory Networks Incorporating Multi-Source Biological Knowledge via a State Space Model with L1 Regularization
Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in the field of systems biology. Currently, there are two main approaches in GRN analysis using time-course observation data, namely an ordinary differential equation (ODE)-based approach and a statistical model-base...
Autores principales: | Hasegawa, Takanori, Yamaguchi, Rui, Nagasaki, Masao, Miyano, Satoru, Imoto, Seiya |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4146587/ https://www.ncbi.nlm.nih.gov/pubmed/25162401 http://dx.doi.org/10.1371/journal.pone.0105942 |
Ejemplares similares
-
Recursive regularization for inferring gene networks from time-course gene expression profiles
por: Shimamura, Teppei, et al.
Publicado: (2009) -
Gene Set-Based Module Discovery Decodes cis-Regulatory Codes Governing Diverse Gene Expression across Human Multiple Tissues
por: Niida, Atsushi, et al.
Publicado: (2010) -
Genomic data assimilation using a higher moment filtering technique for restoration of gene regulatory networks
por: Hasegawa, Takanori, et al.
Publicado: (2015) -
Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing
por: Kojima, Kaname, et al.
Publicado: (2012) -
ExonMiner: Web service for analysis of GeneChip Exon array data
por: Numata, Kazuyuki, et al.
Publicado: (2008)