Evaluation and comparison of multi-omics data integration methods for cancer subtyping.

ABSTRACT: Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, questions of practical importance remain to be addressed regarding the impact of selecting appropriate data types and combinations on the performance of integrative studies. Here, we constructed three classes of benchmarking datasets of nine cancers in TCGA by considering all the eleven combinations of four multi-omics data types. Using these datasets, we conducted a comprehensive evaluation of ten representative integration methods for cancer subtyping in terms of accuracy measured by combining bo

SUBMITTER: Duan R 

PROVIDER: S-EPMC8384175 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature