Breast cancer patient stratification using a molecular regularized consensus clustering method.

ABSTRACT: Breast cancers are highly heterogeneous with different subtypes that lead to different clinical outcomes including prognosis, response to treatment and chances of recurrence and metastasis. An important task in personalized medicine is to determine the subtype for a breast cancer patient in order to provide the most effective treatment. In order to achieve this goal, integrative genomics approach has been developed recently with multiple modalities of large datasets ranging from genotypes to multiple levels of phenotypes. A major challenge in integrative genomics is how to effectively integrate multiple modalities of data to stratify the breast cancer patients. Consensus clustering algorithms have often been adopted for this purpose. However, existing consensus clustering algorithms are no

SUBMITTER: Wang C 

PROVIDER: S-EPMC4151565 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature