Data integration by fuzzy similarity-based hierarchical clustering.

ABSTRACT:

Background

High throughput methods, in biological and biomedical fields, acquire a large number of molecular parameters or omics data by a single experiment. Combining these omics data can significantly increase the capability for recovering fine-tuned structures or reducing the effects of experimental and biological noise in data.

Results

In this work we propose a multi-view integration methodology (named FH-Clust) for identifying patient subgroups from different omics information (e.g., Gene Expression, Mirna Expression, Methylation). In particular, hierarchical structures of patient data are obtained in each omic (or view) and finally their topologies are merged by consensus matrix. One of the main aspects of this methodology, is the use of a measure of dissimilarity betw

SUBMITTER: Ciaramella A 

PROVIDER: S-EPMC7446192 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature