An integrative sparse boosting analysis of cancer genomic commonality and difference.

ABSTRACT: In cancer research, high-throughput profiling has been extensively conducted. In recent studies, the integrative analysis of data on multiple cancer patient groups/subgroups has been conducted. Such analysis has the potential to reveal the genomic commonality as well as difference across groups/subgroups. However, in the existing literature, methods with a special attention to the genomic commonality and difference are very limited. In this study, a novel estimation and marker selection method based on the sparse boosting technique is developed to address the commonality/difference problem. In terms of technical innovation, a new penalty and computation of increments are introduced. The proposed method can also effectively accommodate the grouping structure of covariates. Simulation shows

SUBMITTER: Sun Y 

PROVIDER: S-EPMC7471599 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature