Cargando…
Augmenting Microarray Data with Literature-Based Knowledge to Enhance Gene Regulatory Network Inference
Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important...
Autores principales: | Chen, Guocai, Cairelli, Michael J., Kilicoglu, Halil, Shin, Dongwook, Rindflesch, Thomas C. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4055569/ https://www.ncbi.nlm.nih.gov/pubmed/24921649 http://dx.doi.org/10.1371/journal.pcbi.1003666 |
Ejemplares similares
-
Exploiting Literature-derived Knowledge and Semantics to Identify Potential Prostate Cancer Drugs
por: Zhang, Rui, et al.
Publicado: (2014) -
Sortal anaphora resolution to enhance relation extraction from biomedical literature
por: Kilicoglu, Halil, et al.
Publicado: (2016) -
Investigating the role of interleukin-1 beta and glutamate in inflammatory bowel disease and epilepsy using discovery browsing
por: Rindflesch, Thomas C., et al.
Publicado: (2018) -
Assigning factuality values to semantic relations extracted from biomedical research literature
por: Kilicoglu, Halil, et al.
Publicado: (2017) -
Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks
por: Sîrbu, Alina, et al.
Publicado: (2015)