Integration analysis of three omics data using penalized regression methods: An application to bladder cancer (Methylation)

ABSTRACT: Omics data integration is becoming necessary to investigate the still unknown genomic mechanisms of complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological mechanisms. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we applied penalized regression methods (LASSO and ENET) to explore relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples and have proposed a permutation-based method to concomitantly assess significance and correct by

ORGANISM(S): Homo sapiens

SUBMITTER: Nuria Malats 

PROVIDER: E-GEOD-71666 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress