Cargando…
Hybrid coexpression link similarity graph clustering for mining biological modules from multiple gene expression datasets
BACKGROUND: Advances in genomic technologies have enabled the accumulation of vast amount of genomic data, including gene expression data for multiple species under various biological and environmental conditions. Integration of these gene expression datasets is a promising strategy to alleviate the...
Autores principales: | Salem, Saeed, Ozcaglar, Cagri |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4151083/ https://www.ncbi.nlm.nih.gov/pubmed/25221624 http://dx.doi.org/10.1186/1756-0381-7-16 |
Ejemplares similares
-
Mining for coexpression across hundreds of datasets using novel rank aggregation and visualization methods
por: Adler, Priit, et al.
Publicado: (2009) -
GraphWeb: mining heterogeneous biological networks for gene modules with functional significance
por: Reimand, Jüri, et al.
Publicado: (2008) -
Sublineage structure analysis of Mycobacterium tuberculosis complex strains using multiple-biomarker tensors
por: Ozcaglar, Cagri, et al.
Publicado: (2011) -
Graph mining for next generation sequencing: leveraging the assembly graph for biological insights
por: Warnke-Sommer, Julia, et al.
Publicado: (2016) -
DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques
por: Thafar, Maha A., et al.
Publicado: (2020)