Divisive hierarchical maximum likelihood clustering.

ABSTRACT: BACKGROUND:Biological data comprises various topologies or a mixture of forms, which makes its analysis extremely complicated. With this data increasing in a daily basis, the design and development of efficient and accurate statistical methods has become absolutely necessary. Specific analyses, such as those related to genome-wide association studies and multi-omics information, are often aimed at clustering sub-conditions of cancers and other diseases. Hierarchical clustering methods, which can be categorized into agglomerative and divisive, have been widely used in such situations. However, unlike agglomerative methods divisive clustering approaches have consistently proved to be computationally expensive. RESULTS:The proposed clustering algorithm (DRAGON) was verified on mutation and mi

SUBMITTER: Sharma A 

PROVIDER: S-EPMC5751574 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature