Identification of genes for complex diseases using integrated analysis of multiple types of genomic data.
Various types of genomic data (e.g., SNPs and mRNA transcripts) have been employed to identify risk genes for complex diseases. However, the analysis of these data has largely been performed in isolation. Combining these multiple data for integrative analysis can take advantage of complementary info...
Guardado en:
Autores principales: | Hongbao Cao, Shufeng Lei, Hong-Wen Deng, Yu-Ping Wang |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Public Library of Science (PLoS)
2012
|
Materias: | |
Acceso en línea: | https://doaj.org/article/a0c4983cd43347b9acc36946ec2c57b9 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction
por: Xuan Zhou, et al.
Publicado: (2021) -
Genome-wide Identification and Analysis of Splicing QTLs in Multiple Sclerosis by RNA-Seq Data
por: Yijie He, et al.
Publicado: (2021) -
Integrated functional, gene expression and genomic analysis for the identification of cancer targets.
por: Elizabeth Iorns, et al.
Publicado: (2009) -
Genome-wide identification of directed gene networks using large-scale population genomics data
por: René Luijk, et al.
Publicado: (2018) -
A comparative analysis of gene-expression data of multiple cancer types.
por: Kun Xu, et al.
Publicado: (2010)